NiCad6 Clone Report

System:   GHData Clone pairs:   72948 Clone classes:   3327
Clone type:   3-2 Granularity:   functions-blind Max diff threshold:   30% Clone size:   10 - 2500 lines Total functions-blind:   66305

Class 1:   2 fragments, nominal size 34 lines, similarity 74%

GHData/koenry_Flask-PyTorch-Chatbot/chat.py: 5-50 GHData/abdulghaffaransari_AI-Chat-Bot-Using-PyTorch/processor.py: 10-57

Class 2:   12 fragments, nominal size 21 lines, similarity 70%

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GHData/m0sys_resnet-from-torch/train.py: 20-49 GHData/DongKeon_PyTorch-VAD/train.py: 20-49 GHData/tqxli_breast_ultrasound_lesion_segmentation_PyTorch/train.py: 22-58
GHData/dhruvvpatel_PyTorch-template/train.py: 25-64 GHData/julian-8897_Conv-VAE-PyTorch/train.py: 22-58 GHData/SilentStars_torch_template/train.py: 21-56
GHData/ydon1111_torchTemplate/train.py: 21-56 GHData/pedrob37_PhysicsPyTorch/train.py: 24-63 GHData/thanhhau097_torch-ocr/train.py: 14-52

Class 3:   11 fragments, nominal size 14 lines, similarity 71%

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GHData/raheelqader_torchtext_lite/test.py: 130-151 GHData/raheelqader_torchtext_lite/test.py: 83-104 GHData/raheelqader_torchtext_lite/test.py: 106-129 GHData/raheelqader_torchtext_lite/test.py: 240-263 GHData/raheelqader_torchtext_lite/test.py: 264-288
GHData/raheelqader_torchtext_lite/test.py: 290-313

Class 4:   2 fragments, nominal size 25 lines, similarity 84%

GHData/raheelqader_torchtext_lite/test.py: 458-496 GHData/raheelqader_torchtext_lite/test.py: 497-535

Class 5:   2 fragments, nominal size 11 lines, similarity 81%

GHData/iArunava_IMDB-Sentiment-Analysis-using-PyTorch/SentimentRNN.py: 25-44 GHData/plweegie_PyTorch_RNN/tv_scripts_rnn.py: 32-56

Class 6:   2 fragments, nominal size 73 lines, similarity 98%

GHData/DetectionBLWX_ssdetection/train.py: 30-116 GHData/DetectionBLWX_FPN.pytorch/train.py: 30-117

Class 7:   4 fragments, nominal size 10 lines, similarity 80%

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Class 8:   2 fragments, nominal size 70 lines, similarity 100%

GHData/DetectionBLWX_ssdetection/demo.py: 32-108 GHData/DetectionBLWX_FPN.pytorch/demo.py: 34-110

Class 9:   2 fragments, nominal size 77 lines, similarity 100%

GHData/DetectionBLWX_ssdetection/test.py: 31-117 GHData/DetectionBLWX_FPN.pytorch/test.py: 33-119

Class 10:   4 fragments, nominal size 30 lines, similarity 90%

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Class 11:   675 fragments, nominal size 14 lines, similarity 70%

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GHData/Lornatang_FSRCNN-PyTorch/imgproc.py: 102-173 GHData/Lornatang_SCN-PyTorch/imgproc.py: 106-182 GHData/Lornatang_DRLN-PyTorch/imgproc.py: 105-181
GHData/Lornatang_SRCNN-PyTorch/imgproc.py: 108-185 GHData/Lornatang_ESRGAN-PyTorch/imgproc.py: 53-129 GHData/Lornatang_EDSR-PyTorch/imgproc.py: 102-172
GHData/Lornatang_RFB_ESRGAN-PyTorch/imgproc.py: 107-184 GHData/Lornatang_CARN-PyTorch/imgproc.py: 106-182 GHData/Lornatang_LIIF-PyTorch/imgproc.py: 55-131
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GHData/Lornatang_CSNLN-PyTorch/imgproc.py: 108-185

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GHData/Lornatang_RCAN-PyTorch/imgproc.py: 199-287 GHData/Lornatang_CARN-PyTorch/imgproc.py: 183-271 GHData/Lornatang_LSRGAN-PyTorch/imgproc.py: 197-285
GHData/Lornatang_SCN-PyTorch/imgproc.py: 183-271 GHData/Lornatang_RFB_ESRGAN-PyTorch/imgproc.py: 185-273 GHData/Lornatang_LTE-PyTorch/imgproc.py: 217-305
GHData/Lornatang_IDN-PyTorch/imgproc.py: 183-271 GHData/xiaokai11_ESRGAN-PyTorch/imgproc.py: 186-274 GHData/sdecoder_ESRGAN-PyTorch/imgproc.py: 186-274
GHData/Lornatang_MSRN-PyTorch/imgproc.py: 183-271 GHData/Lornatang_DRLN-PyTorch/imgproc.py: 182-270 GHData/Lornatang_SRCNN-PyTorch/imgproc.py: 186-274
GHData/Lornatang_MetaSR-PyTorch/imgproc.py: 277-365 GHData/Lornatang_ESRGAN-PyTorch/imgproc.py: 202-290 GHData/Lornatang_RDN-PyTorch/imgproc.py: 202-290
GHData/Lornatang_EDSR-PyTorch/imgproc.py: 173-259 GHData/Lornatang_VDSR-PyTorch/imgproc.py: 174-260 GHData/Lornatang_SRGAN-PyTorch/imgproc.py: 202-290
GHData/Lornatang_FSRCNN-PyTorch/imgproc.py: 174-260 GHData/Lornatang_SFTMD-PyTorch/imgproc.py: 387-473 GHData/Lornatang_ArbSR-PyTorch/imgproc.py: 203-297
GHData/Lornatang_CSNLN-PyTorch/imgproc.py: 186-274

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GHData/Lornatang_SCN-PyTorch/imgproc.py: 390-417 GHData/Lornatang_CARN-PyTorch/imgproc.py: 418-445 GHData/Lornatang_DBPN-PyTorch/imgproc.py: 389-416
GHData/Lornatang_MSRN-PyTorch/imgproc.py: 390-417 GHData/Lornatang_SRCNN-PyTorch/imgproc.py: 397-424 GHData/Lornatang_CARN-PyTorch/imgproc.py: 390-417
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GHData/Lornatang_LSRGAN-PyTorch/imgproc.py: 404-431 GHData/Lornatang_LSRGAN-PyTorch/imgproc.py: 432-459 GHData/xiaokai11_ESRGAN-PyTorch/imgproc.py: 397-424
GHData/Lornatang_DRLN-PyTorch/imgproc.py: 421-448 GHData/Lornatang_CSNLN-PyTorch/imgproc.py: 397-424 GHData/Lornatang_RFB_ESRGAN-PyTorch/imgproc.py: 396-423
GHData/sdecoder_ESRGAN-PyTorch/imgproc.py: 425-452 GHData/Lornatang_DRLN-PyTorch/imgproc.py: 393-420

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GHData/Lornatang_LSRGAN-PyTorch/imgproc.py: 506-534 GHData/xiaokai11_ESRGAN-PyTorch/imgproc.py: 499-527 GHData/Lornatang_CycleGAN-PyTorch/imgproc.py: 148-176 GHData/Lornatang_RFB_ESRGAN-PyTorch/imgproc.py: 498-526 GHData/Lornatang_DRLN-PyTorch/imgproc.py: 495-523
GHData/Lornatang_RCAN-PyTorch/imgproc.py: 508-536 GHData/Lornatang_ArbSR-PyTorch/imgproc.py: 518-546 GHData/Lornatang_SCN-PyTorch/imgproc.py: 492-520 GHData/Lornatang_DBPN-PyTorch/imgproc.py: 491-519 GHData/Lornatang_LIIF-PyTorch/imgproc.py: 526-554
GHData/Lornatang_MSRN-PyTorch/imgproc.py: 492-520 GHData/sdecoder_ESRGAN-PyTorch/imgproc.py: 499-527 GHData/Lornatang_IDN-PyTorch/imgproc.py: 492-520 GHData/Lornatang_ESRGAN-PyTorch/imgproc.py: 511-539 GHData/Lornatang_CSNLN-PyTorch/imgproc.py: 499-527
GHData/Lornatang_RDN-PyTorch/imgproc.py: 511-539 GHData/Lornatang_CARN-PyTorch/imgproc.py: 492-520

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GHData/Lornatang_IDN-PyTorch/inference.py: 25-62 GHData/xiaokai11_ESRGAN-PyTorch/inference.py: 25-62 GHData/Lornatang_CARN-PyTorch/inference.py: 25-62
GHData/sdecoder_ESRGAN-PyTorch/inference.py: 25-62

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GHData/Lornatang_MSRN-PyTorch/test.py: 31-111 GHData/sdecoder_ESRGAN-PyTorch/test.py: 27-109 GHData/Lornatang_SCN-PyTorch/test.py: 31-111 GHData/xiaokai11_ESRGAN-PyTorch/test.py: 27-109 GHData/Lornatang_RFB_ESRGAN-PyTorch/validate.py: 27-109
GHData/Lornatang_RDN-PyTorch/test.py: 32-100 GHData/Lornatang_LSRGAN-PyTorch/test.py: 31-102 GHData/Lornatang_DBPN-PyTorch/validate.py: 26-112 GHData/Lornatang_ESRGAN-PyTorch/test.py: 31-102 GHData/Lornatang_SRGAN-PyTorch/test.py: 31-101
GHData/Lornatang_ArbSR-PyTorch/test.py: 31-104 GHData/Lornatang_DSGAN-PyTorch/test.py: 31-99 GHData/Lornatang_ESPCN-PyTorch/test.py: 32-103 GHData/Lornatang_BSRGAN-PyTorch/test.py: 31-88 GHData/Lornatang_Real_ESRGAN-PyTorch/test.py: 27-98

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GHData/Lornatang_SRGAN-PyTorch/dataset.py: 57-81 GHData/Lornatang_SRCNN-PyTorch/dataset.py: 52-77 GHData/Lornatang_LSRGAN-PyTorch/dataset.py: 53-81
GHData/Lornatang_RDN-PyTorch/dataset.py: 57-84 GHData/Lornatang_CARN-PyTorch/dataset.py: 51-78 GHData/Lornatang_RFB_ESRGAN-PyTorch/dataset.py: 54-81
GHData/Lornatang_IDN-PyTorch/dataset.py: 51-78 GHData/sdecoder_ESRGAN-PyTorch/dataset.py: 54-81 GHData/Lornatang_MSRN-PyTorch/dataset.py: 53-81
GHData/Lornatang_DRLN-PyTorch/dataset.py: 54-81 GHData/Lornatang_ESRGAN-PyTorch/dataset.py: 57-84 GHData/Lornatang_SCN-PyTorch/dataset.py: 53-81
GHData/xiaokai11_ESRGAN-PyTorch/dataset.py: 54-81 GHData/Lornatang_SFTMD-PyTorch/dataset.py: 65-93 GHData/Lornatang_DBPN-PyTorch/dataset.py: 53-83
GHData/Lornatang_MetaSR-PyTorch/dataset.py: 53-76 GHData/Lornatang_DSGAN-PyTorch/dataset.py: 60-88 GHData/Lornatang_ESPCN-PyTorch/dataset.py: 57-81
GHData/Lornatang_FSRCNN-PyTorch/dataset.py: 53-78 GHData/Lornatang_RCAN-PyTorch/dataset.py: 54-76 GHData/Lornatang_LIIF-PyTorch/dataset.py: 58-90
GHData/Lornatang_LTE-PyTorch/dataset.py: 58-90 GHData/Lornatang_BSRGAN-PyTorch/dataset.py: 64-93

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GHData/AndreaCossu_PytTorch-NMT/Decoder.py: 56-77 GHData/tewei_PlayTorch/seq2seq.py: 202-218

Class 355:   2 fragments, nominal size 10 lines, similarity 80%

GHData/AndreaCossu_PytTorch-NMT/utils.py: 68-86 GHData/tewei_PlayTorch/plots.py: 31-44

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GHData/AmitNativ1984_YOLOv3-Torch2TRT/models.py: 141-154 GHData/DocF_YOLOv3-Torch2TRT/models.py: 141-154

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GHData/AmitNativ1984_YOLOv3-Torch2TRT/models.py: 380-396 GHData/DocF_YOLOv3-Torch2TRT/models.py: 380-396

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Class 360:   2 fragments, nominal size 42 lines, similarity 100%

GHData/AmitNativ1984_YOLOv3-Torch2TRT/detect.py: 28-77 GHData/DocF_YOLOv3-Torch2TRT/detect.py: 28-78

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Class 362:   4 fragments, nominal size 17 lines, similarity 100%

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GHData/Mark-Sang_PyTorchSaveAnd-Restore/PyTorchSaveAndRestore.py: 10-35 GHData/Smallflyfly_Learning-pytorch/movan3.4.py: 12-37 GHData/wormhole2019_Python-PyTorch/3.4SaveTorch.py: 11-31 GHData/lienguang0624_PyTorch_Test/SaveAndLoad.py: 10-39 GHData/YZJ6GitHub_PyTorch_Learing/torch_save_model.py: 9-33

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GHData/aitorzip_PyTorch-SRGAN/models.py: 82-103 GHData/Subhasom_SRGAN-PyTorch/models.py: 82-103 GHData/bnusss_SRGAN-PyTorch/models.py: 83-104

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GHData/Subhasom_SRGAN-PyTorch/models.py: 104-116 GHData/aitorzip_PyTorch-SRGAN/models.py: 104-116 GHData/fengye-lu_PyTorch-SRGAN/models.py: 104-116

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GHData/aitorzip_PyTorch-SRGAN/utils.py: 13-28 GHData/fengye-lu_PyTorch-SRGAN/utils.py: 13-28 GHData/ljwa2323_PyTorch-SRGAN-master/utils.py: 15-30

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GHData/aitorzip_PyTorch-SRGAN/utils.py: 29-50 GHData/fengye-lu_PyTorch-SRGAN/utils.py: 29-50 GHData/bnusss_SRGAN-PyTorch/utils.py: 31-53

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GHData/dyhan0920_PyramidNet-PyTorch/PyramidNet.py: 17-27 GHData/Chaiyanchong_CutMix-PyTorch-master/pyramidnet.py: 16-26 GHData/leichenNUSJ_AAMandDCM/networks_adaCBMA_deform.py: 56-69 GHData/ilex-paraguariensis_torch_modules/resnet3d.py: 27-39
GHData/Windxy_Classic_Network_PyTorch/ResNet18.py: 23-47

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GHData/Chaiyanchong_CutMix-PyTorch-master/pyramidnet.py: 72-105 GHData/clovaai_CutMix-PyTorch/pyramidnet.py: 72-105 GHData/dyhan0920_PyramidNet-PyTorch/PyramidNet.py: 28-56

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GHData/foamliu_InsightFace-PyTorch/models.py: 189-205 GHData/tristandb_EfficientDet-PyTorch/retinanet.py: 206-222 GHData/miraclewkf_SENet-PyTorch/se_resnext.py: 99-115
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GHData/Sbaig3229_PyTorch-implementation-of-L2CS-Net-without-CUDA/model.py: 36-52 GHData/sunlanchang_YOLOv1-PyTorch/resnet_yolo.py: 155-171 GHData/yaoceyi_CenterNet-PyTorch/model.py: 143-163
GHData/haotian-liu_torch-localization/Resnet.py: 68-84 GHData/dyhan0920_PyramidNet-PyTorch/resnet.py: 132-148 GHData/ISwordLion_ResNets_PyTorch_IR_Pedestrian_Images/drn_anlatim.py: 155-169
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GHData/sofiacatalan_PyTorchTest/transforms.py: 31-46 GHData/mmedlin1997_detect-nn-tl-torch/transforms.py: 31-46 GHData/LuisEstebanAcevedoBringas_FCOS_torch/transformation.py: 26-40
GHData/SunYW0108_demo_torch_MaskRCNN/transforms.py: 31-46 GHData/apacha_MusicObjectDetector-TorchVision/transforms.py: 31-46 GHData/elonashatri_torchvision_MOD/transforms.py: 31-46
GHData/haochen23_Faster-RCNN-fine-tune-PyTorch/transforms.py: 31-46 GHData/finnickniu_Torchvison_Object_Detection/transforms.py: 32-46

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GHData/jweir136_PyTorch-Autoencoder-Latent-Space-Visualization/MNISTConvVAEDataVisualization.py: 36-61 GHData/jweir136_PyTorch-Autoencoder-Latent-Space-Visualization/MNISTConvVAE.py: 47-72 GHData/jweir136_PyTorch-Autoencoder-Latent-Space-Visualization/MNISTConvDataGeneratonTest.py: 36-61

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GHData/TerYang_TorchGAN-large-parallel-train/WGAN_parallel.py: 16-41 GHData/TerYang_TorchGAN-your-lung/nets.py: 27-52 GHData/TerYang_TorchGAN-your-lung/ACGAN.py: 20-46 GHData/TerYang_TorchGAN-your-lung/LSGAN.py: 21-46 GHData/TerYang_TorchGAN-your-lung/WGAN.py: 13-38

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GHData/TerYang_TorchGAN-your-lung/GAN.py: 70-94 GHData/TerYang_TorchGAN-your-lung/WGAN.py: 50-74 GHData/TerYang_TorchGAN-large-parallel-train/WGAN_parallel.py: 53-77 GHData/TerYang_TorchGAN-your-lung/ACGAN.py: 58-87 GHData/TerYang_TorchGAN-large-parallel-train/LSGAN_parallel.py: 123-147

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GHData/TerYang_TorchGAN-your-lung/GAN.py: 105-184 GHData/TerYang_TorchGAN-large-parallel-train/DRAGAN_parallel.py: 82-165 GHData/TerYang_TorchGAN-your-lung/BEGAN.py: 77-126
GHData/TerYang_TorchGAN-large-parallel-train/WGAN_GP_parallel.py: 16-84 GHData/TerYang_TorchGAN-your-lung/GANwithLabel.py: 106-168 GHData/TerYang_TorchGAN-your-lung/LSGAN.py: 92-140
GHData/TerYang_TorchGAN-large-parallel-train/WGAN_parallel.py: 87-157 GHData/TerYang_TorchGAN-your-lung/DRAGAN.py: 78-127 GHData/TerYang_TorchGAN-your-lung/GANnoSchedulerwithLabel.py: 106-167
GHData/TerYang_TorchGAN-your-lung/WGAN_GP.py: 15-61 GHData/TerYang_TorchGAN-your-lung/WGAN.py: 84-130

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GHData/TerYang_TorchGAN-your-lung/GANwithLabel.py: 169-287 GHData/TerYang_TorchGAN-your-lung/GAN.py: 185-310 GHData/TerYang_TorchGAN-large-parallel-train/LSGAN_parallel.py: 237-349
GHData/TerYang_TorchGAN-your-lung/WGAN.py: 131-234 GHData/TerYang_TorchGAN-large-parallel-train/WGAN_GP_parallel.py: 86-233 GHData/TerYang_TorchGAN-your-lung/LSGAN.py: 141-265
GHData/TerYang_TorchGAN-your-lung/WGAN_GP.py: 62-193 GHData/TerYang_TorchGAN-large-parallel-train/DRAGAN_parallel.py: 167-306 GHData/TerYang_TorchGAN-your-lung/DRAGAN.py: 128-252

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GHData/TerYang_TorchGAN-your-lung/LSGAN.py: 266-294 GHData/TerYang_TorchGAN-large-parallel-train/LSGAN_parallel.py: 350-378 GHData/TerYang_TorchGAN-your-lung/GANnoSchedulerwithLabel.py: 272-300
GHData/TerYang_TorchGAN-large-parallel-train/WGAN_parallel.py: 279-307 GHData/TerYang_TorchGAN-your-lung/GAN.py: 311-339 GHData/TerYang_TorchGAN-your-lung/WGAN_GP.py: 194-222
GHData/TerYang_TorchGAN-your-lung/WGAN.py: 235-263 GHData/TerYang_TorchGAN-your-lung/DRAGAN.py: 253-281 GHData/TerYang_TorchGAN-large-parallel-train/DRAGAN_parallel.py: 307-335
GHData/TerYang_TorchGAN-your-lung/BEGAN.py: 266-294

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GHData/TerYang_TorchGAN-large-parallel-train/LSGAN_parallel.py: 414-438 GHData/TerYang_TorchGAN-your-lung/GAN.py: 373-398 GHData/TerYang_TorchGAN-large-parallel-train/BEGAN_parallel.py: 338-362 GHData/TerYang_TorchGAN-large-parallel-train/DRAGAN_parallel.py: 371-395

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GHData/lyp741_PyTorch-ActorCriticRL-master/model.py: 15-39 GHData/lyp741_PyTorch-ActorCriticRL-master/model.py: 60-84 GHData/lyp741_PyTorch-ActorCriticRL-master/pytorch-ddpg.py: 120-144

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GHData/erickingxu_pyBridge/torchLayer_IMPL.py: 316-390 GHData/erickingxu_pyBridge/torchLayer_IMPL.py: 581-648 GHData/erickingxu_pyBridge/torchLayer_IMPL.py: 1305-1370
GHData/erickingxu_pyBridge/torchLayer_IMPL.py: 649-719 GHData/erickingxu_pyBridge/torchLayer_IMPL.py: 246-315 GHData/erickingxu_pyBridge/torchLayer_IMPL.py: 814-881
GHData/erickingxu_pyBridge/torchLayer_IMPL.py: 882-947 GHData/erickingxu_pyBridge/torchLayer_IMPL.py: 948-1014 GHData/erickingxu_pyBridge/torchLayer_IMPL.py: 1221-1304
GHData/erickingxu_pyBridge/torchLayer_IMPL.py: 720-813

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GHData/Lornatang_YOLOv1-PyTorch/model.py: 95-108 GHData/dbbbbm_f-AnoGAN-PyTorch/wgan64x64.py: 207-219 GHData/woojoo99_torch_CAM/vgg.py: 51-64
GHData/undefinedXD_PyTorch-CIFAR100-Adversarial-Attack/new_utils.py: 246-271 GHData/kir3i_MLwithPyTorch/14_VGG.py: 141-155 GHData/tomron27_PyTorch_Test/vgg.py: 49-62
GHData/vidursatija_PhotoWCT/custom_vgg16.py: 54-67 GHData/vidursatija_PhotoWCT/custom_vgg16.py: 146-159 GHData/dbbbbm_f-AnoGAN-PyTorch/wgan64x64.py: 256-268

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GHData/BaoWentz_AdvancedEAST-PyTorch/model_VGG.py: 44-57 GHData/ppx-hub_PyTorch_VGG16_Cifar10/main.py: 41-56 GHData/salmanmaq_VGG-PyTorch/vgg.py: 41-57 GHData/tomron27_PyTorch_Test/vgg.py: 63-78

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GHData/zhaoyuzhi_Non-Local-Block-PyTorch/main.py: 78-102 GHData/Lornatang_PyTorch-WGANGP/gan_mnist.py: 161-182 GHData/tiruota_WGAN-PyTorch/wgan.py: 126-149

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GHData/happyConan_Torch_Gan/torch_6.py: 120-145 GHData/happyConan_Torch_Gan/torch_7.py: 120-145 GHData/HAOLI-TUKL_Deep_Learning_PyTorch/DCGAN.py: 41-67
GHData/KostasTok_keras_DCGAN_transfer_learning/torch_dcgan.py: 37-63 GHData/Lc-1024_PyTorch-Learning/fake_image_DCGAN.py: 92-122 GHData/andrew-bydlon_PyTorch/GANamp.py: 49-74
GHData/andrew-bydlon_PyTorch/GAN.py: 50-75 GHData/XuLongjia_PyTorchLearning/11DCGAN.py: 44-70 GHData/rohan1561_Torch-Implementations-of-Basic-DL-Models/GAN.py: 63-86
GHData/Xiaoctw_praTorch/file17.py: 67-92 GHData/qianyuqianxun-DeepLearning_DCGAN_Pytorch/dc_utils.py: 27-52 GHData/wutianyiRosun_VAE_GAN_PyTorch/dcgan.py: 106-131

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GHData/andrew-bydlon_PyTorch/GAN.py: 84-106 GHData/XuLongjia_PyTorchLearning/11DCGAN.py: 87-109 GHData/HAOLI-TUKL_Deep_Learning_PyTorch/DCGAN.py: 83-105
GHData/qianyuqianxun-DeepLearning_DCGAN_Pytorch/dc_utils.py: 58-81 GHData/Xiaoctw_praTorch/file17.py: 100-123 GHData/rohan1561_Torch-Implementations-of-Basic-DL-Models/GAN.py: 93-116
GHData/happyConan_Torch_Gan/torch_7.py: 162-185 GHData/Lc-1024_PyTorch-Learning/fake_image_DCGAN.py: 58-86 GHData/happyConan_Torch_Gan/torch_6.py: 162-185
GHData/yoshua133_low-dose-pet/main.py: 242-268 GHData/Lornatang_PyTorch-WGANGP/gan_mnist.py: 124-140 GHData/Lornatang_PyTorch-WGANGP/gan_cifar.py: 134-150

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GHData/dongwhfdyer_JVTC_ms/t_resnet.py: 12-24 GHData/sunlanchang_YOLOv1-PyTorch/resnet_yolo.py: 62-74 GHData/foamliu_InsightFace-PyTorch/models.py: 64-76 GHData/Xingxiangrui_various_pyTorch_network_structure/group_clsgat_parallel.py: 84-102

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GHData/IanSullivan_DeepFakeTorch/SSIM.py: 20-42 GHData/langmanbusi_KinD_PyTorch/loss.py: 99-121 GHData/Sebastian514_DeepfakeTorchthings/SSIM.py: 20-42

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GHData/IanSullivan_DeepFakeTorch/SSIM.py: 51-68 GHData/zhangyi-3_torchlib/metrics.py: 50-66 GHData/Sebastian514_DeepfakeTorchthings/SSIM.py: 51-68

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GHData/PhilippKitz_PyTorch/mnist.py: 79-94 GHData/BenjaminAm_PyTorch-Tutorial/Tutorial2MNIST.py: 74-88

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GHData/gagansingh894_Deep-Learning-with-PyTorch/fc_model.py: 40-59 GHData/hdvvip_Deep_Learning_PyTorch/fc_model.py: 40-60 GHData/goksinan_Intro-to-PyTorch/fc_model.py: 41-61
GHData/nriteshranjan_Deep-Learning-with-PyTorch/fc_model.py: 40-60

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GHData/yearing1017_PyTorch_Note/Pytorch_CNN.py: 37-51 GHData/rayan-yu_PyTorch-CNN-MNIST/TestCNN.py: 44-57 GHData/wtergan_PyTorch-Summer2022/cnn.py: 37-50 GHData/jaehyunnn_PyTorch-Practice/05_torch_CNN.py: 37-52 GHData/yoonhero_torch_learning/cnn.py: 31-55
GHData/kir3i_MLwithPyTorch/11_Convolution_Neural_Network.py: 26-40

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GHData/yanjingke_PPO-PyTorch/PPO_continuous.py: 138-226 GHData/yanjingke_PPO-PyTorch/PPO.py: 133-216

Class 582:   4 fragments, nominal size 11 lines, similarity 76%

GHData/foamliu_Car-Recognition-PyTorch/utils.py: 83-96 GHData/foamliu_Look-Into-Person-PyTorch/utils.py: 112-127 GHData/foamliu_Image-Colorization-PyTorch/utils.py: 83-96 GHData/foamliu_Deep-Image-Matting-PyTorch/utils.py: 84-99

Class 583:   2 fragments, nominal size 17 lines, similarity 76%

GHData/wenzhilong_test_PyTorch/LeNet.py: 40-61 GHData/hliangzhao_Torch-Tools/metrics.py: 55-80

Class 584:   2 fragments, nominal size 21 lines, similarity 85%

GHData/wenzhilong_test_PyTorch/LeNet.py: 62-84 GHData/hliangzhao_Torch-Tools/metrics.py: 353-378

Class 585:   8 fragments, nominal size 12 lines, similarity 83%

GHData/CheQiXiao_MoFan-Net/save_restore.py: 56-72 GHData/erika1203_torch/save.py: 59-78 GHData/lienguang0624_PyTorch_Test/SaveAndLoad.py: 50-64 GHData/Smallflyfly_Learning-pytorch/movan3.4.py: 47-61
GHData/Silent-voice_PyTorch/save_load.py: 51-70 GHData/leoriohope_pyTorch_learn/3.4_model_save.py: 53-68 GHData/YZJ6GitHub_PyTorch_Learing/torch_save_model.py: 43-57 GHData/Mark-Sang_PyTorchSaveAnd-Restore/PyTorchSaveAndRestore.py: 45-60

Class 586:   2 fragments, nominal size 17 lines, similarity 88%

GHData/kawori_Noise2Noise_PyTorch/train.py: 28-46 GHData/kawori_Noise2Noise_PyTorch/train_clean.py: 27-43

Class 587:   2 fragments, nominal size 12 lines, similarity 83%

GHData/yourFriendlyNeighbour_Torchman/util.py: 376-401 GHData/yourFriendlyNeighbour_Torchman/util.py: 402-427

Class 588:   7 fragments, nominal size 14 lines, similarity 100%

GHData/yourFriendlyNeighbour_Torchman/ai.py: 76-91 GHData/Gvence_Self-Driving-Car-PyTorch/ai.py: 76-90 GHData/Olembo_Modeling-and-Simulation-of-an-autonomous-car-based-on-Deep-Q-Learning-using-PyTorch-Kivy/ai2.py: 80-94
GHData/MagaliDrumare_How-to-create-a-self-driving-car-with-PyTorch-DQL-/ai.py: 76-90 GHData/dmkaner_SelfDrivingCarPyTorchSim/ai2.py: 76-90 GHData/dmkaner_SelfDrivingCarPyTorchSim/ai.py: 76-90
GHData/reetc_self_drivin_sim/ai.py: 80-94

Class 589:   2 fragments, nominal size 13 lines, similarity 100%

GHData/yourFriendlyNeighbour_Torchman/TorchAgents.py: 164-178 GHData/yourFriendlyNeighbour_Torchman/keyboardAgents.py: 105-120

Class 590:   12 fragments, nominal size 17 lines, similarity 73%

GHData/yjh0410_yolov2-yolov3_PyTorch/eval.py: 41-66 GHData/yjh0410_PyTorch_AnchorYOLO/eval.py: 63-84 GHData/wuxiaolianggit_PyTorch_FCOS/eval.py: 58-79
GHData/yifuxiong_DETR-PyTorch/eval.py: 67-89 GHData/yjh0410_PyTorch_YOLOv1/eval.py: 50-76 GHData/yjh0410_new-YOLOv1_PyTorch/eval.py: 39-64
GHData/liuyundong-2020_yolov3-plus_PyTorch/eval.py: 45-70 GHData/yjh0410_FCOS-RT_PyTorch/eval.py: 54-79 GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/eval.py: 41-66
GHData/yjh0410_PyTorch_YOLOv3/eval.py: 56-82 GHData/yjh0410_PyTorch_YOLOv2/eval.py: 56-82 GHData/dreamplus1989_yolov3-plus_PyTorch/eval_coco.py: 48-74

Class 591:   3 fragments, nominal size 54 lines, similarity 73%

GHData/yjh0410_yolov2-yolov3_PyTorch/train.py: 32-96 GHData/yjh0410_FCOS-RT_PyTorch/train.py: 29-98 GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/train.py: 31-95

Class 592:   3 fragments, nominal size 237 lines, similarity 72%

GHData/yjh0410_yolov2-yolov3_PyTorch/train.py: 97-456 GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/train.py: 96-443 GHData/liuyundong-2020_yolov3-plus_PyTorch/train.py: 79-392

Class 593:   4 fragments, nominal size 18 lines, similarity 72%

GHData/yjh0410_yolov2-yolov3_PyTorch/train.py: 462-485 GHData/liuyundong-2020_yolov3-plus_PyTorch/train.py: 398-422 GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/train.py: 449-472 GHData/dreamplus1989_yolov3-plus_PyTorch/train_coco.py: 346-374

Class 594:   2 fragments, nominal size 25 lines, similarity 100%

GHData/yjh0410_yolov2-yolov3_PyTorch/demo.py: 12-41 GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/demo.py: 12-41

Class 595:   6 fragments, nominal size 97 lines, similarity 72%

GHData/yjh0410_yolov2-yolov3_PyTorch/demo.py: 68-195 GHData/liuyundong-2020_yolov3-plus_PyTorch/demo.py: 59-188 GHData/yifuxiong_DETR-PyTorch/demo.py: 112-239
GHData/yifuxiong_DETR-PyTorch/demo_bc.py: 112-239 GHData/yjh0410_PyTorch_AnchorYOLO/demo.py: 79-217 GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/demo.py: 68-195

Class 596:   2 fragments, nominal size 49 lines, similarity 100%

GHData/yjh0410_yolov2-yolov3_PyTorch/demo.py: 196-265 GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/demo.py: 196-265

Class 597:   9 fragments, nominal size 25 lines, similarity 100%

GHData/yjh0410_yolov2-yolov3_PyTorch/test.py: 59-87 GHData/yjh0410_PyTorch_YOLOv3/test.py: 62-90 GHData/yjh0410_FCOS-RT_PyTorch/test.py: 69-97 GHData/yjh0410_PyTorch_YOLOv1/test.py: 58-86
GHData/yifuxiong_DETR-PyTorch/test.py: 88-116 GHData/yjh0410_PyTorch_AnchorYOLO/test.py: 82-110 GHData/wuxiaolianggit_PyTorch_FCOS/test.py: 74-102 GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/test.py: 59-87
GHData/yjh0410_PyTorch_YOLOv2/test.py: 62-90

Class 598:   6 fragments, nominal size 39 lines, similarity 79%

GHData/yjh0410_yolov2-yolov3_PyTorch/test.py: 88-138 GHData/yjh0410_FCOS-RT_PyTorch/test.py: 98-156 GHData/yifuxiong_DETR-PyTorch/test.py: 117-169
GHData/wuxiaolianggit_PyTorch_FCOS/test.py: 103-159 GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/test.py: 88-138 GHData/yjh0410_PyTorch_AnchorYOLO/test.py: 112-164

Class 599:   5 fragments, nominal size 13 lines, similarity 84%

GHData/yjh0410_yolov2-yolov3_PyTorch/tools.py: 15-34 GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/tools.py: 15-34 GHData/yjh0410_PyTorch_YOLOv1/tools.py: 12-30 GHData/yjh0410_PyTorch_YOLOv3/tools.py: 15-34 GHData/yjh0410_PyTorch_YOLOv2/tools.py: 15-34

Class 600:   6 fragments, nominal size 22 lines, similarity 100%

GHData/yjh0410_yolov2-yolov3_PyTorch/tools.py: 35-75 GHData/liuyundong-2020_yolov3-plus_PyTorch/tools.py: 64-104 GHData/yjh0410_PyTorch_YOLOv2/tools.py: 35-76
GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/tools.py: 35-75 GHData/dreamplus1989_yolov3-plus_PyTorch/tools.py: 91-131 GHData/yjh0410_PyTorch_YOLOv3/tools.py: 35-75

Class 601:   3 fragments, nominal size 44 lines, similarity 100%

GHData/yjh0410_yolov2-yolov3_PyTorch/tools.py: 95-164 GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/tools.py: 95-164 GHData/yjh0410_PyTorch_YOLOv2/tools.py: 100-171

Class 602:   3 fragments, nominal size 28 lines, similarity 89%

GHData/yjh0410_yolov2-yolov3_PyTorch/tools.py: 165-217 GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/tools.py: 165-217 GHData/yjh0410_PyTorch_YOLOv2/tools.py: 172-208

Class 603:   5 fragments, nominal size 78 lines, similarity 80%

GHData/yjh0410_yolov2-yolov3_PyTorch/tools.py: 218-339 GHData/liuyundong-2020_yolov3-plus_PyTorch/tools.py: 124-230 GHData/dreamplus1989_yolov3-plus_PyTorch/tools.py: 151-271 GHData/yjh0410_PyTorch_YOLOv3/tools.py: 95-214 GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/tools.py: 218-339

Class 604:   5 fragments, nominal size 26 lines, similarity 70%

GHData/yjh0410_yolov2-yolov3_PyTorch/tools.py: 355-397 GHData/yjh0410_PyTorch_YOLOv3/tools.py: 230-272 GHData/yjh0410_PyTorch_YOLOv2/tools.py: 224-266 GHData/wzwtime_openCV-yolov2-yolov3_PyTorch/tools.py: 355-397 GHData/yjh0410_PyTorch_YOLOv1/tools.py: 89-126

Class 605:   4 fragments, nominal size 65 lines, similarity 96%

GHData/yearing1017_CCNet_PyTorch/train_kfold.py: 14-123 GHData/yearing1017_DANet_PyTorch/train_v3_danet.py: 16-125 GHData/yearing1017_DANet_PyTorch/train_danet_res.py: 13-122 GHData/yearing1017_CCNet_PyTorch/train_cc_v3_0509.py: 17-126

Class 606:   2 fragments, nominal size 11 lines, similarity 100%

GHData/yearing1017_CCNet_PyTorch/predict.py: 18-29 GHData/yearing1017_DANet_PyTorch/predict.py: 16-27

Class 607:   3 fragments, nominal size 14 lines, similarity 92%

GHData/yearing1017_CCNet_PyTorch/predict.py: 36-55 GHData/yearing1017_DANet_PyTorch/predict.py: 34-53 GHData/yearing1017_DANet_PyTorch/predict_gray.py: 34-51

Class 608:   2 fragments, nominal size 29 lines, similarity 72%

GHData/TheInfamousWayne_ddpg_torch/test.py: 4-38 GHData/TheInfamousWayne_ddpg_torch/test.py: 39-74

Class 609:   2 fragments, nominal size 15 lines, similarity 81%

GHData/VHCC_PyTorch-age-estimation/train.py: 24-41 GHData/VHCC_PyTorch-age-estimation/test.py: 18-32

Class 610:   3 fragments, nominal size 13 lines, similarity 100%

GHData/splionar_image-sim-torch/main.py: 34-53 GHData/splionar_image-sim-torch/main.py: 321-340 GHData/splionar_image-sim-torch/main.py: 96-115

Class 611:   2 fragments, nominal size 15 lines, similarity 93%

GHData/splionar_image-sim-torch/layers.py: 12-28 GHData/splionar_image-sim-torch/layers.py: 35-51

Class 612:   2 fragments, nominal size 16 lines, similarity 87%

GHData/erilyth_Text-Generation-PyTorch/namesPredictionTest.py: 39-58 GHData/GerogeZhi_PyTorch/generating%20names.py: 166-183

Class 613:   2 fragments, nominal size 11 lines, similarity 100%

GHData/erilyth_Text-Generation-PyTorch/namesPredictionTrain.py: 46-62 GHData/GerogeZhi_PyTorch/generating%20names.py: 119-130

Class 614:   3 fragments, nominal size 33 lines, similarity 100%

GHData/Kodamayuto2001_PyTorch_Simple_Neural_Network/train.py: 26-65 GHData/Kodamayuto2001_PyTorch_CNN/train_cnn_dataaugmentation.py: 57-96 GHData/Kodamayuto2001_PyTorch_CNN/train_cnn.py: 57-96

Class 615:   3 fragments, nominal size 18 lines, similarity 88%

GHData/Kodamayuto2001_PyTorch_Simple_Neural_Network/train.py: 124-152 GHData/Kodamayuto2001_PyTorch_CNN/train_cnn_dataaugmentation.py: 161-190 GHData/Kodamayuto2001_PyTorch_CNN/train_cnn.py: 159-188

Class 616:   4 fragments, nominal size 10 lines, similarity 81%

GHData/shinoyuki222_Flask/utils.py: 85-94 GHData/faportillo_Torch-Bot/utils.py: 205-216 GHData/Doragd_Chinese-Chatbot-PyTorch-Implementation/dataload.py: 15-29 GHData/Ninzore_Chinese_Chatbot_Torch/corpus_gen.py: 36-80

Class 617:   2 fragments, nominal size 16 lines, similarity 87%

GHData/KoryBurns_torchgraph/torch_graphconv.py: 149-173 GHData/KoryBurns_torchgraph/torch_graphconv.py: 304-332

Class 618:   2 fragments, nominal size 24 lines, similarity 100%

GHData/KoryBurns_torchgraph/torch_graphconv.py: 174-213 GHData/KoryBurns_torchgraph/torch_graphconv.py: 356-389

Class 619:   2 fragments, nominal size 61 lines, similarity 100%

GHData/KoryBurns_torchgraph/torch_graphconv.py: 214-297 GHData/KoryBurns_torchgraph/torch_graphconv.py: 390-471

Class 620:   2 fragments, nominal size 37 lines, similarity 75%

GHData/skrish13_CrossTransformers-PyTorch/main.py: 60-129 GHData/skrish13_CrossTransformers-PyTorch/main.py: 130-200

Class 621:   10 fragments, nominal size 16 lines, similarity 76%

GHData/skrish13_CrossTransformers-PyTorch/resnet.py: 38-55 GHData/vujadeyoon_TensorRT-Torch2TRT/resnet.py: 39-56 GHData/Chaanks_stklia/models.py: 33-52 GHData/zwx8981_DBCNN-PyTorch/SCNN3.py: 76-93 GHData/JimpeiYamamoto_myTorch/myResNet50.py: 19-35
GHData/JimpeiYamamoto_myTorch/MCDropout.py: 39-55 GHData/TalentBoy2333_RetinaNet-PyTorch-Tutorial/resnet.py: 26-43 GHData/bo-10000_ResNet-D_PyTorch/resnetD.py: 40-56 GHData/bo-10000_ResNet-D_PyTorch/resnetD_3d.py: 40-56 GHData/jsesr_CSE-GResNet-PyTorch/GResNet.py: 26-44

Class 622:   8 fragments, nominal size 15 lines, similarity 73%

GHData/skrish13_CrossTransformers-PyTorch/resnet.py: 84-100 GHData/jsesr_CSE-GResNet-PyTorch/GResNet.py: 67-83 GHData/bo-10000_ResNet-D_PyTorch/resnetD.py: 79-94 GHData/bo-10000_ResNet-D_PyTorch/resnetD_3d.py: 79-94
GHData/TalentBoy2333_RetinaNet-PyTorch-Tutorial/resnet.py: 72-88 GHData/JimpeiYamamoto_myTorch/myResNet50.py: 54-69 GHData/vujadeyoon_TensorRT-Torch2TRT/resnet.py: 85-101 GHData/JimpeiYamamoto_myTorch/MCDropout.py: 74-99

Class 623:   6 fragments, nominal size 43 lines, similarity 75%

GHData/skrish13_CrossTransformers-PyTorch/resnet.py: 126-176 GHData/JimpeiYamamoto_myTorch/MCDropout.py: 127-187 GHData/vujadeyoon_TensorRT-Torch2TRT/resnet.py: 127-177
GHData/JimpeiYamamoto_myTorch/myResNet50.py: 206-269 GHData/TalentBoy2333_RetinaNet-PyTorch-Tutorial/resnet.py: 114-166 GHData/JimpeiYamamoto_myTorch/myResNet50.py: 88-148

Class 624:   10 fragments, nominal size 20 lines, similarity 71%

GHData/skrish13_CrossTransformers-PyTorch/resnet.py: 177-200 GHData/vujadeyoon_TensorRT-Torch2TRT/resnet.py: 178-201 GHData/jsesr_CSE-GResNet-PyTorch/GResNet.py: 161-184 GHData/JimpeiYamamoto_myTorch/myResNet50.py: 270-293 GHData/JimpeiYamamoto_myTorch/myResNet50.py: 149-172
GHData/JimpeiYamamoto_myTorch/MCDropout.py: 188-211 GHData/TalentBoy2333_RetinaNet-PyTorch-Tutorial/resnet.py: 167-190 GHData/Chaanks_stklia/models.py: 124-147 GHData/zwx8981_DBCNN-PyTorch/SCNN3.py: 161-184 GHData/DGenady_gw_torch/resnetNoBN.py: 99-120

Class 625:   2 fragments, nominal size 12 lines, similarity 100%

GHData/nurpeiis_LeakGAN-PyTorch/main.py: 100-113 GHData/nurpeiis_LeakGAN-PyTorch/train.py: 23-35

Class 626:   2 fragments, nominal size 10 lines, similarity 90%

GHData/amirmallak_Licence-Plate-Detection-PyTorch/neural_network_model.py: 48-62 GHData/bearbearyu1223_Get-Started-with-PyTorch/simple_linear_regression_example.py: 35-47

Class 627:   2 fragments, nominal size 29 lines, similarity 89%

GHData/tszdanger_torch_grammartest/jd_fenci.py: 93-131 GHData/taowenyin_HelloTorch/S3.py: 20-62

Class 628:   5 fragments, nominal size 10 lines, similarity 75%

GHData/tszdanger_torch_grammartest/antorbee.py: 19-37 GHData/goys94_MalariaNet-for-PyTorch/final.py: 100-114 GHData/elliottzheng_PyTorchExample/train.py: 35-47 GHData/CalumMacLellan1995_U-net-PyTorch/cell_dataset_2cells.py: 108-119 GHData/DerekGloudemans_torchvision-classifiers/tv_test.py: 89-109

Class 629:   4 fragments, nominal size 18 lines, similarity 100%

GHData/gicsaw_ARAE_torch/CARAE_test.py: 20-42 GHData/gicsaw_ARAE_torch/ARAE_test.py: 20-42 GHData/gicsaw_ARAE_torch/CARAE_train.py: 20-42 GHData/gicsaw_ARAE_torch/ARAE_train.py: 20-42

Class 630:   4 fragments, nominal size 12 lines, similarity 100%

GHData/gicsaw_ARAE_torch/CARAE_test.py: 50-65 GHData/gicsaw_ARAE_torch/ARAE_train.py: 50-65 GHData/gicsaw_ARAE_torch/ARAE_test.py: 50-65 GHData/gicsaw_ARAE_torch/CARAE_train.py: 50-65

Class 631:   2 fragments, nominal size 11 lines, similarity 100%

GHData/gicsaw_ARAE_torch/CARAE_test.py: 67-80 GHData/gicsaw_ARAE_torch/CARAE_train.py: 67-81

Class 632:   2 fragments, nominal size 135 lines, similarity 74%

GHData/gicsaw_ARAE_torch/CARAE_test.py: 89-289 GHData/gicsaw_ARAE_torch/ARAE_test.py: 89-249

Class 633:   2 fragments, nominal size 17 lines, similarity 82%

GHData/gicsaw_ARAE_torch/valid.py: 36-61 GHData/gicsaw_ARAE_torch/valid.py: 62-84

Class 634:   2 fragments, nominal size 76 lines, similarity 78%

GHData/gicsaw_ARAE_torch/CARAE_gen.py: 26-145 GHData/gicsaw_ARAE_torch/ARAE_gen.py: 26-122

Class 635:   3 fragments, nominal size 13 lines, similarity 92%

GHData/hieubkvn123_ArcFacePyTorch/train.py: 86-100 GHData/hieubkvn123_FaceRecognitionPyTorch/arcface_net.py: 26-40 GHData/hieubkvn123_ArcFacePyTorch/arcface_net.py: 16-30

Class 636:   3 fragments, nominal size 13 lines, similarity 92%

GHData/hieubkvn123_ArcFacePyTorch/train.py: 101-120 GHData/hieubkvn123_FaceRecognitionPyTorch/arcface_net.py: 41-60 GHData/hieubkvn123_ArcFacePyTorch/arcface_net.py: 31-50

Class 637:   3 fragments, nominal size 11 lines, similarity 100%

GHData/hieubkvn123_ArcFacePyTorch/train.py: 122-139 GHData/hieubkvn123_FaceRecognitionPyTorch/arcface_net.py: 62-79 GHData/hieubkvn123_ArcFacePyTorch/arcface_net.py: 52-69

Class 638:   2 fragments, nominal size 60 lines, similarity 100%

GHData/hieubkvn123_ArcFacePyTorch/face_align.py: 23-132 GHData/hieubkvn123_FaceRecognitionPyTorch/face_align.py: 23-132

Class 639:   2 fragments, nominal size 40 lines, similarity 70%

GHData/sssharaf_torch_hub/hubconf.py: 73-129 GHData/sssharaf_torch_hub/hubconf.py: 167-224

Class 640:   3 fragments, nominal size 26 lines, similarity 70%

GHData/sssharaf_torch_hub/hubconf.py: 130-165 GHData/sssharaf_torch_hub/hubconf.py: 225-262 GHData/sssharaf_torch_hub/hubconf.py: 305-333

Class 641:   3 fragments, nominal size 20 lines, similarity 71%

GHData/sssharaf_torch_hub/hubconf.py: 335-361 GHData/sssharaf_torch_hub/hubconf.py: 378-405 GHData/sssharaf_torch_hub/hubconf.py: 423-453

Class 642:   2 fragments, nominal size 20 lines, similarity 85%

GHData/erikreppel_PyTorch-tools/experiment.py: 108-138 GHData/erikreppel_PyTorch-tools/experiment.py: 149-175

Class 643:   3 fragments, nominal size 13 lines, similarity 85%

GHData/aaronespasa_ImageSegmentation-PyTorch/train.py: 36-57 GHData/DoganK01_PyTorch-U-Net-From-Scratch/train.py: 40-61 GHData/vence-andersen_UNet-PyTorch/train.py: 32-54

Class 644:   2 fragments, nominal size 53 lines, similarity 92%

GHData/aaronespasa_ImageSegmentation-PyTorch/train.py: 58-127 GHData/DoganK01_PyTorch-U-Net-From-Scratch/train.py: 62-131

Class 645:   3 fragments, nominal size 12 lines, similarity 91%

GHData/DateBro_PSGAN-PyTorch/net.py: 126-139 GHData/SDU-MagicMirror_PSGAN-PyTorch/my_psgan.py: 16-28 GHData/SDU-MagicMirror_PSGAN-PyTorch/net.py: 126-139

Class 646:   2 fragments, nominal size 43 lines, similarity 100%

GHData/DateBro_PSGAN-PyTorch/net.py: 179-242 GHData/SDU-MagicMirror_PSGAN-PyTorch/net.py: 179-242

Class 647:   2 fragments, nominal size 12 lines, similarity 100%

GHData/DateBro_PSGAN-PyTorch/net.py: 244-265 GHData/SDU-MagicMirror_PSGAN-PyTorch/net.py: 244-265

Class 648:   5 fragments, nominal size 27 lines, similarity 82%

GHData/DateBro_PSGAN-PyTorch/net.py: 267-307 GHData/SDU-MagicMirror_PSGAN-PyTorch/net.py: 267-307 GHData/SDU-MagicMirror_PSGAN-PyTorch/makeup_gan.py: 300-336 GHData/SDU-MagicMirror_PSGAN-PyTorch/my_psgan.py: 229-263 GHData/DateBro_PSGAN-PyTorch/makeup_gan.py: 300-336

Class 649:   2 fragments, nominal size 40 lines, similarity 100%

GHData/DateBro_PSGAN-PyTorch/net.py: 308-368 GHData/SDU-MagicMirror_PSGAN-PyTorch/net.py: 308-368

Class 650:   3 fragments, nominal size 27 lines, similarity 88%

GHData/DateBro_PSGAN-PyTorch/makeup_gan.py: 140-178 GHData/SDU-MagicMirror_PSGAN-PyTorch/my_psgan.py: 143-180 GHData/SDU-MagicMirror_PSGAN-PyTorch/makeup_gan.py: 140-178

Class 651:   2 fragments, nominal size 12 lines, similarity 100%

GHData/DateBro_PSGAN-PyTorch/makeup_gan.py: 179-192 GHData/SDU-MagicMirror_PSGAN-PyTorch/makeup_gan.py: 179-192

Class 652:   3 fragments, nominal size 13 lines, similarity 76%

GHData/DateBro_PSGAN-PyTorch/makeup_gan.py: 197-215 GHData/SDU-MagicMirror_PSGAN-PyTorch/makeup_gan.py: 197-215 GHData/SDU-MagicMirror_PSGAN-PyTorch/my_psgan.py: 193-211

Class 653:   2 fragments, nominal size 12 lines, similarity 100%

GHData/DateBro_PSGAN-PyTorch/makeup_gan.py: 229-247 GHData/SDU-MagicMirror_PSGAN-PyTorch/makeup_gan.py: 229-247

Class 654:   2 fragments, nominal size 17 lines, similarity 100%

GHData/DateBro_PSGAN-PyTorch/makeup_gan.py: 264-283 GHData/SDU-MagicMirror_PSGAN-PyTorch/makeup_gan.py: 264-283

Class 655:   2 fragments, nominal size 11 lines, similarity 100%

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GHData/alex-lechner_PyTorch-SSD/ssd.py: 198-209

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GHData/201419_Optimizer-PyTorch/sgd.py: 40-54

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GHData/mickymicmouse_v_OCGAN_torch/utility.py: 150-178 GHData/mickymicmouse_v_OCGAN_torch/utility.py: 179-216

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GHData/mickymicmouse_v_OCGAN_torch/dataset.py: 19-114 GHData/mickymicmouse_v_OCGAN_torch/dataset.py: 115-210

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GHData/dragen1860_DARTS-PyTorch/model.py: 93-108 GHData/dragen1860_DARTS-PyTorch/model.py: 117-133

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GHData/Shawn-Guo-CN_YOLOv3_PyTorch/utils.py: 181-210 GHData/youssefshoeb_YOLOv3-PyTorch/darknet.py: 323-355 GHData/AdityaKarn_Yolo_torch/darknet.py: 324-356

Class 768:   3 fragments, nominal size 80 lines, similarity 91%

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GHData/SSusantAchary_Image_Captioning_using_PyTorch/model.py: 44-56 GHData/nikunjlad_Image-Captioning-using-PyTorch/model.py: 44-56

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GHData/Jintao-Huang_FasterRCNN_PyTorch/train.py: 32-48 GHData/Jintao-Huang_EfficientDet_PyTorch/train.py: 28-60 GHData/Jintao-Huang_EfficientDet_PyTorch/train_VOC0712.py: 37-53

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GHData/LeifYaoYuXiang_PyTorch_Template/utils.py: 9-21 GHData/chengjie11_riiid-transformer/utils.py: 24-35 GHData/keyu-tian_torch_test/misc.py: 14-25

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GHData/Hung-Ta-Chen_Implement-CNN-for-Image-Recognition-with-PyTorch/cnn_mnist.py: 136-159 GHData/Hung-Ta-Chen_Implement-CNN-for-Image-Recognition-with-PyTorch/cnn_cifar.py: 238-268 GHData/Hung-Ta-Chen_Implement-CNN-for-Image-Recognition-with-PyTorch/cnn_mnist.py: 221-252 GHData/Hung-Ta-Chen_Implement-CNN-for-Image-Recognition-with-PyTorch/cnn_cifar.py: 176-201

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GHData/skmhrk1209_DCGAN-PyTorch/model.py: 7-59 GHData/skmhrk1209_DCGAN-PyTorch/model.py: 66-120

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GHData/Lornatang_ESRGAN-PyTorch/train_rrdbnet.py: 173-187 GHData/Lornatang_BSRGAN-PyTorch/train_bsrnet.py: 172-186 GHData/Lornatang_DSGAN-PyTorch/train.py: 201-217

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GHData/Lornatang_LTE-PyTorch/train.py: 284-364 GHData/Lornatang_LSRGAN-PyTorch/train_lsrgan.py: 423-493 GHData/Lornatang_ESRGAN-PyTorch/train_rrdbnet.py: 289-359 GHData/Lornatang_LIIF-PyTorch/train.py: 283-363
GHData/Lornatang_SCN-PyTorch/train.py: 289-359 GHData/Lornatang_DSGAN-PyTorch/train.py: 402-472 GHData/Lornatang_SRGAN-PyTorch/train_srgan.py: 400-469 GHData/Lornatang_RDN-PyTorch/train.py: 281-350
GHData/Lornatang_SRGAN-PyTorch/train_srresnet.py: 259-329 GHData/Lornatang_MSRN-PyTorch/train.py: 289-359 GHData/Lornatang_ArbSR-PyTorch/train.py: 302-371 GHData/Lornatang_ESPCN-PyTorch/train.py: 268-338

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GHData/Lornatang_DSGAN-PyTorch/train.py: 35-169 GHData/Lornatang_ESRGAN-PyTorch/train_esrgan.py: 37-178 GHData/Lornatang_SRGAN-PyTorch/train_srgan.py: 35-169

Class 810:   2 fragments, nominal size 12 lines, similarity 100%

GHData/Lornatang_LSRGAN-PyTorch/train_lsrgan.py: 206-222 GHData/Lornatang_ESRGAN-PyTorch/train_esrgan.py: 210-226

Class 811:   5 fragments, nominal size 10 lines, similarity 70%

GHData/Lornatang_LSRGAN-PyTorch/train_lsrgan.py: 223-237 GHData/Lornatang_ESRGAN-PyTorch/train_esrgan.py: 227-241 GHData/Lornatang_BSRGAN-PyTorch/train_bsrgan.py: 231-245 GHData/Lornatang_SRGAN-PyTorch/train_srgan.py: 213-227 GHData/Lornatang_Real_ESRGAN-PyTorch/train_realesrgan.py: 227-241

Class 812:   6 fragments, nominal size 11 lines, similarity 75%

GHData/Lornatang_LSRGAN-PyTorch/train_lsrgan.py: 238-249 GHData/Lornatang_SRGAN-PyTorch/train_srgan.py: 228-242 GHData/Lornatang_ESRGAN-PyTorch/train_esrgan.py: 242-256
GHData/Lornatang_BSRGAN-PyTorch/train_bsrgan.py: 246-260 GHData/Lornatang_DSGAN-PyTorch/train.py: 238-252 GHData/Lornatang_Real_ESRGAN-PyTorch/train_realesrgan.py: 242-256

Class 813:   4 fragments, nominal size 11 lines, similarity 100%

GHData/Lornatang_LSRGAN-PyTorch/train_lsrgan.py: 250-262 GHData/Lornatang_SRGAN-PyTorch/train_srgan.py: 243-255 GHData/Lornatang_BSRGAN-PyTorch/train_bsrgan.py: 261-273 GHData/Lornatang_ESRGAN-PyTorch/train_esrgan.py: 257-269

Class 814:   6 fragments, nominal size 94 lines, similarity 72%

GHData/Lornatang_LSRGAN-PyTorch/train_lsrgan.py: 263-422 GHData/Lornatang_BSRGAN-PyTorch/train_bsrgan.py: 274-438 GHData/Lornatang_DSGAN-PyTorch/train.py: 267-401
GHData/Lornatang_SRGAN-PyTorch/train_srgan.py: 256-399 GHData/Lornatang_ESRGAN-PyTorch/train_esrgan.py: 270-429 GHData/Lornatang_Real_ESRGAN-PyTorch/train_realesrgan.py: 269-470

Class 815:   8 fragments, nominal size 14 lines, similarity 92%

GHData/Lornatang_LSRGAN-PyTorch/inference.py: 51-78 GHData/Lornatang_ESRGAN-PyTorch/inference.py: 51-78 GHData/Lornatang_DSGAN-PyTorch/inference.py: 50-77 GHData/Lornatang_RDN-PyTorch/inference.py: 49-76
GHData/Lornatang_Real_ESRGAN-PyTorch/inference.py: 47-74 GHData/Lornatang_BSRGAN-PyTorch/inference.py: 51-78 GHData/Lornatang_SRGAN-PyTorch/inference.py: 50-77 GHData/Lornatang_ArbSR-PyTorch/inference.py: 54-81

Class 816:   4 fragments, nominal size 28 lines, similarity 70%

GHData/TodoListIOS_NER-PyTorch/BiLSTM_CRF.py: 11-56 GHData/opatrickchen_NER-PyTorch/BiLSTM.py: 6-40 GHData/TodoListIOS_NER-PyTorch/BiLSTM.py: 6-40 GHData/opatrickchen_NER-PyTorch/BiLSTM_CRF.py: 11-56

Class 817:   2 fragments, nominal size 45 lines, similarity 100%

GHData/TodoListIOS_NER-PyTorch/BiLSTM_CRF.py: 63-127 GHData/opatrickchen_NER-PyTorch/BiLSTM_CRF.py: 63-127

Class 818:   2 fragments, nominal size 13 lines, similarity 100%

GHData/TodoListIOS_NER-PyTorch/BiLSTM_CRF.py: 162-186 GHData/opatrickchen_NER-PyTorch/BiLSTM_CRF.py: 162-186

Class 819:   2 fragments, nominal size 11 lines, similarity 100%

GHData/TodoListIOS_NER-PyTorch/BiLSTM_CRF.py: 187-211 GHData/opatrickchen_NER-PyTorch/BiLSTM_CRF.py: 187-211

Class 820:   4 fragments, nominal size 14 lines, similarity 86%

GHData/TodoListIOS_NER-PyTorch/BiLSTM_CRF.py: 212-231 GHData/opatrickchen_NER-PyTorch/BiLSTM.py: 80-111 GHData/opatrickchen_NER-PyTorch/BiLSTM_CRF.py: 212-231 GHData/TodoListIOS_NER-PyTorch/BiLSTM.py: 80-111

Class 821:   2 fragments, nominal size 24 lines, similarity 100%

GHData/TodoListIOS_NER-PyTorch/BiLSTM_CRF.py: 232-267 GHData/opatrickchen_NER-PyTorch/BiLSTM_CRF.py: 232-267

Class 822:   2 fragments, nominal size 28 lines, similarity 100%

GHData/TodoListIOS_NER-PyTorch/HMM.py: 23-66 GHData/opatrickchen_NER-PyTorch/HMM.py: 23-66

Class 823:   2 fragments, nominal size 29 lines, similarity 100%

GHData/TodoListIOS_NER-PyTorch/HMM.py: 67-145 GHData/opatrickchen_NER-PyTorch/HMM.py: 67-145

Class 824:   2 fragments, nominal size 31 lines, similarity 100%

GHData/TodoListIOS_NER-PyTorch/data_manager.py: 8-42 GHData/opatrickchen_NER-PyTorch/data_manager.py: 8-42

Class 825:   2 fragments, nominal size 36 lines, similarity 100%

GHData/TodoListIOS_NER-PyTorch/data_manager.py: 56-107 GHData/opatrickchen_NER-PyTorch/data_manager.py: 56-107

Class 826:   2 fragments, nominal size 11 lines, similarity 100%

GHData/TodoListIOS_NER-PyTorch/data_manager.py: 121-134 GHData/opatrickchen_NER-PyTorch/data_manager.py: 121-134

Class 827:   4 fragments, nominal size 47 lines, similarity 89%

GHData/TodoListIOS_NER-PyTorch/BiLSTM_model.py: 16-68 GHData/TodoListIOS_NER-PyTorch/BiLSTM_CRF_model.py: 22-74 GHData/opatrickchen_NER-PyTorch/BiLSTM_CRF_model.py: 22-74 GHData/opatrickchen_NER-PyTorch/BiLSTM_model.py: 16-68

Class 828:   4 fragments, nominal size 22 lines, similarity 100%

GHData/TodoListIOS_NER-PyTorch/BiLSTM_model.py: 69-92 GHData/TodoListIOS_NER-PyTorch/BiLSTM_CRF_model.py: 75-98 GHData/opatrickchen_NER-PyTorch/BiLSTM_model.py: 69-92 GHData/opatrickchen_NER-PyTorch/BiLSTM_CRF_model.py: 75-98

Class 829:   4 fragments, nominal size 30 lines, similarity 86%

GHData/TodoListIOS_NER-PyTorch/BiLSTM_model.py: 118-175 GHData/opatrickchen_NER-PyTorch/BiLSTM_model.py: 118-175 GHData/opatrickchen_NER-PyTorch/BiLSTM_CRF_model.py: 124-179 GHData/TodoListIOS_NER-PyTorch/BiLSTM_CRF_model.py: 124-179

Class 830:   4 fragments, nominal size 43 lines, similarity 95%

GHData/TodoListIOS_NER-PyTorch/BiLSTM_model.py: 176-232 GHData/opatrickchen_NER-PyTorch/BiLSTM_model.py: 176-232 GHData/opatrickchen_NER-PyTorch/BiLSTM_CRF_model.py: 180-235 GHData/TodoListIOS_NER-PyTorch/BiLSTM_CRF_model.py: 180-235

Class 831:   2 fragments, nominal size 26 lines, similarity 100%

GHData/TodoListIOS_NER-PyTorch/HMM_model.py: 14-43 GHData/opatrickchen_NER-PyTorch/HMM_model.py: 14-43

Class 832:   2 fragments, nominal size 14 lines, similarity 100%

GHData/TodoListIOS_NER-PyTorch/HMM_model.py: 44-59 GHData/opatrickchen_NER-PyTorch/HMM_model.py: 44-59

Class 833:   2 fragments, nominal size 10 lines, similarity 100%

GHData/TodoListIOS_NER-PyTorch/HMM_model.py: 74-84 GHData/opatrickchen_NER-PyTorch/HMM_model.py: 74-84

Class 834:   2 fragments, nominal size 23 lines, similarity 100%

GHData/TodoListIOS_NER-PyTorch/HMM_model.py: 89-118 GHData/opatrickchen_NER-PyTorch/HMM_model.py: 89-118

Class 835:   2 fragments, nominal size 13 lines, similarity 76%

GHData/linksense_EfficientNet.PyTorch/efficientnet.py: 35-50 GHData/linksense_MixNet-PyTorch/mixnet.py: 55-71

Class 836:   2 fragments, nominal size 14 lines, similarity 100%

GHData/linksense_EfficientNet.PyTorch/efficientnet.py: 111-140 GHData/linksense_MixNet-PyTorch/mixnet.py: 176-202

Class 837:   2 fragments, nominal size 13 lines, similarity 84%

GHData/linksense_EfficientNet.PyTorch/efficientnet.py: 239-254 GHData/linksense_MixNet-PyTorch/mixnet.py: 329-344

Class 838:   6 fragments, nominal size 16 lines, similarity 76%

GHData/BJohnBraddock_BigGAN-PyTorch/my_train_fns.py: 12-39 GHData/BJohnBraddock_BigGAN-PyTorch/train_fns.py: 20-47 GHData/BJohnBraddock_BigGAN-PyTorch/train_fns.py: 48-75
GHData/BJohnBraddock_BigGAN-PyTorch/my_train_fns.py: 68-95 GHData/BJohnBraddock_BigGAN-PyTorch/my_train_fns.py: 40-67 GHData/BJohnBraddock_BigGAN-PyTorch/train_fns.py: 76-103

Class 839:   2 fragments, nominal size 20 lines, similarity 72%

GHData/BJohnBraddock_BigGAN-PyTorch/my_train_fns.py: 96-126 GHData/BJohnBraddock_BigGAN-PyTorch/train_fns.py: 104-139

Class 840:   2 fragments, nominal size 105 lines, similarity 89%

GHData/BJohnBraddock_BigGAN-PyTorch/finetune_latent_with_vca.py: 18-214 GHData/BJohnBraddock_BigGAN-PyTorch/finetune_latent_with_alexnet.py: 18-207

Class 841:   2 fragments, nominal size 43 lines, similarity 84%

GHData/BJohnBraddock_BigGAN-PyTorch/genetic_algorithm_GAN.py: 116-165 GHData/BJohnBraddock_BigGAN-PyTorch/finetune_class_with_vca.py: 163-215

Class 842:   4 fragments, nominal size 13 lines, similarity 76%

GHData/BJohnBraddock_BigGAN-PyTorch/biggan_v1.py: 59-75 GHData/huangtao36_PyTorch-Fully-Convolutional-ResNet-50/network_module.py: 273-290 GHData/linjx-ustc1106_ZstGAN-PyTorch/model.py: 374-392 GHData/zhaoyuzhi_Non-Local-Block-PyTorch/Spectralnorm.py: 47-64

Class 843:   3 fragments, nominal size 20 lines, similarity 70%

GHData/BJohnBraddock_BigGAN-PyTorch/biggan_v1.py: 198-225 GHData/BJohnBraddock_BigGAN-PyTorch/biggan_v1.py: 245-271 GHData/BJohnBraddock_BigGAN-PyTorch/biggan_v1.py: 291-320

Class 844:   3 fragments, nominal size 14 lines, similarity 100%

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Class 845:   2 fragments, nominal size 19 lines, similarity 75%

GHData/vidursatija_PhotoWCT/custom_vgg16.py: 68-90 GHData/vidursatija_PhotoWCT/custom_vgg16.py: 160-181

Class 846:   2 fragments, nominal size 10 lines, similarity 70%

GHData/vidursatija_PhotoWCT/mat_transforms.py: 11-27 GHData/vidursatija_PhotoWCT/mat_transforms.py: 28-46

Class 847:   2 fragments, nominal size 90 lines, similarity 95%

GHData/salmanmaq_Conv-EncDec-PyTorch/segnet.py: 148-256 GHData/shufanwu_SegNet-PyTorch/weight_transfer.py: 4-95

Class 848:   6 fragments, nominal size 25 lines, similarity 70%

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GHData/myungsanglee_PyTorch-Object-Detection/test_yolov4-tiny.py: 14-45 GHData/myungsanglee_PyTorch-Pose-Estimation/test_sbp.py: 14-56 GHData/myungsanglee_PyTorch-Classification/test_classifier.py: 14-41

Class 849:   8 fragments, nominal size 38 lines, similarity 71%

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GHData/myungsanglee_PyTorch-Object-Detection/train_yolov2.py: 18-73 GHData/myungsanglee_PyTorch-Classification/train_classifier.py: 31-80 GHData/myungsanglee_PyTorch-Pose-Estimation/train_sbp_pis.py: 19-80 GHData/myungsanglee_PyTorch-Object-Detection/train_yolov3.py: 21-76

Class 850:   2 fragments, nominal size 70 lines, similarity 98%

GHData/myungsanglee_PyTorch-Object-Detection/yolo2coco_pred_file_yolov3.py: 19-123 GHData/myungsanglee_PyTorch-Object-Detection/yolo2coco_pred_file_yolov2.py: 19-123

Class 851:   6 fragments, nominal size 56 lines, similarity 70%

GHData/myungsanglee_PyTorch-Object-Detection/inference_yolov2.py: 18-98 GHData/myungsanglee_PyTorch-Object-Detection/inference_yolov4-tiny.py: 17-83 GHData/myungsanglee_PyTorch-Object-Detection/inference_yolov3.py: 19-99
GHData/myungsanglee_PyTorch-Object-Detection/inference_yolov1.py: 21-99 GHData/myungsanglee_PyTorch-Pose-Estimation/inference_spm.py: 17-92 GHData/myungsanglee_PyTorch-Pose-Estimation/inference_sbp.py: 17-111

Class 852:   2 fragments, nominal size 54 lines, similarity 98%

GHData/myungsanglee_PyTorch-Object-Detection/make_pred_file_yolov3.py: 15-88 GHData/myungsanglee_PyTorch-Object-Detection/make_pred_file_yolov2.py: 15-88

Class 853:   2 fragments, nominal size 12 lines, similarity 91%

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Class 854:   2 fragments, nominal size 11 lines, similarity 90%

GHData/kumar-shridhar_PyTorch-Super-Resolution/main.py: 81-94 GHData/kunjan-mhaske_Image-super-resolution-model-using-4-layers-CNN-from-PyTorch/generate_model.py: 89-106

Class 855:   2 fragments, nominal size 32 lines, similarity 100%

GHData/kumar-shridhar_PyTorch-Super-Resolution/scrape_google_search_images.py: 15-47 GHData/andrew-bydlon_PyTorch/Images.py: 15-47

Class 856:   3 fragments, nominal size 16 lines, similarity 100%

GHData/kumar-shridhar_PyTorch-Super-Resolution/data.py: 10-33 GHData/kumar-shridhar_PyTorch-Bayesian-Super-Resolution/data.py: 10-33 GHData/kunjan-mhaske_Image-super-resolution-model-using-4-layers-CNN-from-PyTorch/data.py: 18-43

Class 857:   2 fragments, nominal size 10 lines, similarity 100%

GHData/luuuyi_ShuffleNetV2_vs_MnasNet.PyTorch/train.py: 20-30 GHData/luuuyi_CBAM.PyTorch/train.py: 18-28

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GHData/luuuyi_ShuffleNetV2_vs_MnasNet.PyTorch/train.py: 31-102 GHData/luuuyi_CBAM.PyTorch/train.py: 29-98

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GHData/luuuyi_ShuffleNetV2_vs_MnasNet.PyTorch/test.py: 19-99 GHData/luuuyi_CBAM.PyTorch/test.py: 19-99 GHData/luuuyi_CBAM.PyTorch/test.py: 100-199 GHData/luuuyi_ShuffleNetV2_vs_MnasNet.PyTorch/test.py: 100-199

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Class 861:   12 fragments, nominal size 16 lines, similarity 75%

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GHData/Lornatang_SSD_PyTorch/eval.py: 100-119 GHData/luuuyi_RefineDet.PyTorch/eval_refinedet.py: 106-125 GHData/shivasanketh-rm_PyTorch_SSD/eval.py: 104-123
GHData/sung0471_SSD_PyTorch/eval.py: 103-122 GHData/aksenventwo_ssd_torch/eval.py: 104-123 GHData/dreamplus1989_yolov3-plus_PyTorch/eval_voc.py: 116-135
GHData/yjt2018_RefineDet.PyTorch/eval_refinedet.py: 106-125 GHData/alex-lechner_PyTorch-SSD/eval.py: 96-115 GHData/SaralaSewwandi_RefineDet.PyTorch/eval_refinedet.py: 109-128

Class 862:   14 fragments, nominal size 14 lines, similarity 86%

GHData/wangwang1125_ssd-pytorch-1.5/eval.py: 146-162 GHData/yjt2018_RefineDet.PyTorch/eval_refinedet.py: 148-164 GHData/sung0471_SSD_PyTorch/eval.py: 145-161
GHData/dreamplus1989_yolov3-plus_PyTorch/eval_voc.py: 158-174 GHData/Lornatang_SSD_PyTorch/eval.py: 142-158 GHData/aksenventwo_ssd_torch/eval.py: 146-162
GHData/VissageMo_SSD-Torch/eval.py: 146-162 GHData/alex-lechner_PyTorch-SSD/eval.py: 138-154 GHData/luuuyi_RefineDet.PyTorch/eval_refinedet.py: 148-164
GHData/V-ea_ssd.pytorch.v1/eval_general.py: 178-194 GHData/V-ea_ssd.pytorch.v1/eval.py: 228-246 GHData/shivasanketh-rm_PyTorch_SSD/eval.py: 146-162
GHData/V-ea_ssd.pytorch.v1/eval.py: 211-227 GHData/SaralaSewwandi_RefineDet.PyTorch/eval_refinedet.py: 151-167

Class 863:   13 fragments, nominal size 27 lines, similarity 73%

GHData/wangwang1125_ssd-pytorch-1.5/eval.py: 163-193 GHData/Lornatang_SSD_PyTorch/eval.py: 159-189 GHData/alex-lechner_PyTorch-SSD/eval.py: 155-185
GHData/yjt2018_RefineDet.PyTorch/eval_refinedet.py: 165-195 GHData/aksenventwo_ssd_torch/eval.py: 163-193 GHData/sung0471_SSD_PyTorch/eval.py: 162-192
GHData/shivasanketh-rm_PyTorch_SSD/eval.py: 163-193 GHData/luuuyi_RefineDet.PyTorch/eval_refinedet.py: 165-195 GHData/V-ea_ssd.pytorch.v1/eval.py: 247-278
GHData/dreamplus1989_yolov3-plus_PyTorch/eval_voc.py: 175-205 GHData/VissageMo_SSD-Torch/eval.py: 163-193 GHData/SaralaSewwandi_RefineDet.PyTorch/eval_refinedet.py: 168-198
GHData/V-ea_ssd.pytorch.v1/eval_general.py: 234-262

Class 864:   13 fragments, nominal size 17 lines, similarity 100%

GHData/wangwang1125_ssd-pytorch-1.5/eval.py: 194-227 GHData/mrkieumy_YOLOv3_PyTorch/my_eval.py: 20-52 GHData/aksenventwo_ssd_torch/eval.py: 194-227
GHData/Lornatang_SSD_PyTorch/eval.py: 190-223 GHData/luuuyi_RefineDet.PyTorch/eval_refinedet.py: 196-229 GHData/sunlanchang_YOLOv1-PyTorch/eval_voc.py: 35-59
GHData/alex-lechner_PyTorch-SSD/eval.py: 186-219 GHData/VissageMo_SSD-Torch/eval.py: 194-227 GHData/yjt2018_RefineDet.PyTorch/eval_refinedet.py: 196-229
GHData/sung0471_SSD_PyTorch/eval.py: 193-226 GHData/SaralaSewwandi_RefineDet.PyTorch/eval_refinedet.py: 199-232 GHData/dreamplus1989_yolov3-plus_PyTorch/eval_voc.py: 206-239
GHData/shivasanketh-rm_PyTorch_SSD/eval.py: 194-227

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GHData/wangwang1125_ssd-pytorch-1.5/eval.py: 228-363 GHData/sung0471_SSD_PyTorch/eval.py: 227-362 GHData/dreamplus1989_yolov3-plus_PyTorch/eval_voc.py: 240-352
GHData/SaralaSewwandi_RefineDet.PyTorch/eval_refinedet.py: 233-368 GHData/Lornatang_SSD_PyTorch/eval.py: 224-359 GHData/V-ea_ssd.pytorch.v1/eval_general.py: 287-418
GHData/luuuyi_RefineDet.PyTorch/eval_refinedet.py: 230-365 GHData/alex-lechner_PyTorch-SSD/eval.py: 220-355 GHData/VissageMo_SSD-Torch/eval.py: 228-363
GHData/yjt2018_RefineDet.PyTorch/eval_refinedet.py: 230-365 GHData/aksenventwo_ssd_torch/eval.py: 228-363 GHData/V-ea_ssd.pytorch.v1/eval.py: 474-616
GHData/mrkieumy_YOLOv3_PyTorch/my_eval.py: 53-199 GHData/shivasanketh-rm_PyTorch_SSD/eval.py: 228-363

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GHData/aksenventwo_ssd_torch/eval.py: 364-415 GHData/alex-lechner_PyTorch-SSD/eval.py: 356-407 GHData/Lornatang_SSD_PyTorch/eval.py: 360-411
GHData/luuuyi_RefineDet.PyTorch/eval_refinedet.py: 366-417 GHData/sung0471_SSD_PyTorch/eval.py: 363-418 GHData/yjt2018_RefineDet.PyTorch/eval_refinedet.py: 366-417
GHData/shivasanketh-rm_PyTorch_SSD/eval.py: 364-415 GHData/V-ea_ssd.pytorch.v1/eval.py: 617-668 GHData/SaralaSewwandi_RefineDet.PyTorch/eval_refinedet.py: 369-430

Class 867:   9 fragments, nominal size 105 lines, similarity 70%

GHData/wangwang1125_ssd-pytorch-1.5/train.py: 71-207 GHData/shivasanketh-rm_PyTorch_SSD/train.py: 71-202 GHData/aksenventwo_ssd_torch/train.py: 71-201 GHData/VissageMo_SSD-Torch/train_vhr.py: 74-189
GHData/alex-lechner_PyTorch-SSD/train.py: 105-248 GHData/VissageMo_SSD-Torch/train.py: 71-201 GHData/sung0471_SSD_PyTorch/train.py: 70-213 GHData/Lornatang_SSD_PyTorch/train.py: 77-205
GHData/V-ea_ssd.pytorch.v1/train.py: 71-214

Class 868:   13 fragments, nominal size 13 lines, similarity 92%

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GHData/shivasanketh-rm_PyTorch_SSD/train.py: 237-254 GHData/SaralaSewwandi_RefineDet.PyTorch/train_refinedet.py: 269-286 GHData/luuuyi_RefineDet.PyTorch/train_refinedet.py: 269-286
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GHData/AtalsKim_torchlearning/lstmsq_0918_forshort.py: 118-139

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GHData/AtalsKim_torchlearning/lstmsintest_0917_forshort_forcolab.py: 101-126

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GHData/yuniktmr_Generative-Adversial-Networks-GAN-using-Pytorch-Torchvision-and-Python/dcgan.py: 72-88 GHData/yuniktmr_Generative-Adversial-Networks-GAN-using-Pytorch-Torchvision-and-Python/dcgan_commented.py: 39-57 GHData/yuniktmr_Generative-Adversial-Networks-GAN-using-Pytorch-Torchvision-and-Python/dcgan.py: 44-61 GHData/alex-lechner_PyTorch-GAN/neural_net.py: 8-27 GHData/yuniktmr_Generative-Adversial-Networks-GAN-using-Pytorch-Torchvision-and-Python/dcgan_nocomment.py: 39-57
GHData/LongLong-Jing_PyTorch-UNet/network.py: 9-39

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GHData/jxgu1016_Gabor_CNN_PyTorch/setup.py: 16-57 GHData/krumo_Domain-Adaptive-Faster-RCNN-PyTorch/setup.py: 17-58 GHData/xvdp_torchvision_extra/setup.py: 24-69

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GHData/HuzaifaToor_PyTorch_Image_Classification_MultiGPU/main.py: 102-140 GHData/XingruiWang_torch_template/main.py: 91-125 GHData/JoursBleu_resnet_torch_habana/main.py: 84-122 GHData/kaiyaointel_torchvision/main.py: 81-115 GHData/JoursBleu_resnet_torch_habana/main_hpu.py: 93-133

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GHData/Alexiland_Torch_SI/in6.py: 71-189

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GHData/Yogurt2019_ABD-Net_on_torchreid/torch_image_classification.py: 263-308 GHData/Lornatang_YOLOv1-PyTorch/train_features.py: 271-317 GHData/kaiyaointel_torchvision/main.py: 266-312
GHData/HuzaifaToor_PyTorch_Image_Classification_MultiGPU/train_validate.py: 12-72 GHData/XingruiWang_torch_template/main.py: 276-323 GHData/JoursBleu_resnet_torch_habana/main.py: 302-351
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GHData/JoursBleu_resnet_torch_habana/main_hpu.py: 319-381

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GHData/taniyarajani_CIFAR10_Classification_PyTorch/train_resnet_finetuning.py: 160-215

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GHData/taniyarajani_CIFAR10_Classification_PyTorch/train_resconv.py: 204-312 GHData/taniyarajani_CIFAR10_Classification_PyTorch/train_resnet_soft.py: 215-325 GHData/taniyarajani_CIFAR10_Classification_PyTorch/train_resnet_soft_B.py: 217-327 GHData/taniyarajani_CIFAR10_Classification_PyTorch/train_resnet_finetuning.py: 216-327 GHData/taniyarajani_CIFAR10_Classification_PyTorch/train_resnet_2layer_B.py: 220-329

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GHData/junzhouye_NeuronCoverageTorch/cifar10_resnet.py: 9-22 GHData/HideOnHouse_CIFAR10_Toy/Model.py: 163-176 GHData/DableUTeeF_HiResTorch/resnet20.py: 21-35

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GHData/DableUTeeF_HiResTorch/resnet20.py: 49-64 GHData/sunlanchang_YOLOv1-PyTorch/resnet_yolo.py: 103-118 GHData/junzhouye_NeuronCoverageTorch/cifar10_resnet.py: 34-49

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GHData/mashagua_torch_learn/lesson_5.py: 83-99 GHData/XuLongjia_PyTorchLearning/6TextC_RNN.py: 104-119 GHData/mashagua_torch_learn/lesson_5.py: 100-117

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GHData/clvrai_ACGAN-PyTorch/network.py: 202-254 GHData/jvlk_ACGAN-PyTorch-Chromos/network.py: 68-120 GHData/jvlk_ACGAN-PyTorch-Chromos/network.py: 202-254

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GHData/jvlk_ACGAN-PyTorch-Chromos/network.py: 121-146 GHData/zsypotter_ACGAN-PyTorch/network.py: 255-278 GHData/jvlk_ACGAN-PyTorch-Chromos/network.py: 255-278

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GHData/hei4_TorchDCGAN/main.py: 168-184 GHData/hei4_TorchCGAN/main.py: 184-200 GHData/hei4_TorchCGAN/main.py: 128-145

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Class 1882:   2 fragments, nominal size 10 lines, similarity 100%

GHData/Huanghongru_RTE-GAN/magenerator.py: 508-518 GHData/Huanghongru_RTE-GAN/agenerator.py: 343-353

Class 1883:   2 fragments, nominal size 14 lines, similarity 100%

GHData/Huanghongru_RTE-GAN/aseq2seq.py: 199-218 GHData/Huanghongru_RTE-GAN/agenerator.py: 183-202

Class 1884:   2 fragments, nominal size 18 lines, similarity 73%

GHData/MaximeAeva_PyTorchNeurons/main.py: 79-102 GHData/yshiyi_Deep-Neural-Networks-with-PyTorch/Chapter07_03NNMultiDim.py: 14-34

Class 1885:   2 fragments, nominal size 16 lines, similarity 70%

GHData/thegyro_unsup_keyp_torch/visualizer.py: 81-101 GHData/thegyro_unsup_keyp_torch/visualizer.py: 102-125

Class 1886:   2 fragments, nominal size 16 lines, similarity 70%

GHData/thegyro_unsup_keyp_torch/visualizer.py: 138-159 GHData/thegyro_unsup_keyp_torch/visualizer.py: 437-466

Class 1887:   3 fragments, nominal size 25 lines, similarity 72%

GHData/thegyro_unsup_keyp_torch/visualizer.py: 260-299 GHData/thegyro_unsup_keyp_torch/visualizer.py: 474-514 GHData/thegyro_unsup_keyp_torch/visualizer.py: 428-473

Class 1888:   2 fragments, nominal size 33 lines, similarity 82%

GHData/thegyro_unsup_keyp_torch/test_viz_dyn.py: 16-59 GHData/thegyro_unsup_keyp_torch/test_viz_dyn.py: 60-105

Class 1889:   2 fragments, nominal size 40 lines, similarity 100%

GHData/thegyro_unsup_keyp_torch/test_viz.py: 113-160 GHData/thegyro_unsup_keyp_torch/test_viz_inv.py: 87-134

Class 1890:   2 fragments, nominal size 23 lines, similarity 73%

GHData/thegyro_unsup_keyp_torch/vision.py: 72-107 GHData/thegyro_unsup_keyp_torch/vision.py: 151-179

Class 1891:   2 fragments, nominal size 23 lines, similarity 72%

GHData/thegyro_unsup_keyp_torch/vision.py: 108-149 GHData/thegyro_unsup_keyp_torch/vision.py: 180-216

Class 1892:   3 fragments, nominal size 17 lines, similarity 70%

GHData/thegyro_unsup_keyp_torch/vision.py: 226-255 GHData/thegyro_unsup_keyp_torch/vision.py: 256-289 GHData/thegyro_unsup_keyp_torch/vision.py: 301-332

Class 1893:   6 fragments, nominal size 60 lines, similarity 70%

GHData/Kodamayuto2001_PyTorchTest05/test5.py: 32-93 GHData/Kodamayuto2001_PyTorchAlexNet/test1.py: 42-113 GHData/Kodamayuto2001_PyTorch7/food1.py: 36-104
GHData/Kodamayuto2001_PyTorchTest05/test7.py: 35-99 GHData/Kodamayuto2001_PyTorchTest05/test6.py: 35-99 GHData/Kodamayuto2001_PyTorchSimpleCNN/cnn.py: 40-113

Class 1894:   2 fragments, nominal size 15 lines, similarity 100%

GHData/Kodamayuto2001_PyTorchTest05/test5.py: 112-128 GHData/Kodamayuto2001_PyTorchTest05/test4.py: 82-98

Class 1895:   6 fragments, nominal size 39 lines, similarity 75%

GHData/Kodamayuto2001_PyTorchTest05/test2.py: 19-63 GHData/Kodamayuto2001_PyTorchTest4/test04.py: 30-75 GHData/Kodamayuto2001_PyTorchTest05/test1.py: 19-63
GHData/Kodamayuto2001_PyTorchTest3/Project5.py: 55-109 GHData/Kodamayuto2001_PyTorchTest3/Project4.py: 30-84 GHData/Kodamayuto2001_PyTorchTest05/test4.py: 19-64

Class 1896:   6 fragments, nominal size 10 lines, similarity 100%

GHData/Kodamayuto2001_PyTorchTest05/test7.py: 126-135 GHData/Kodamayuto2001_PyTorch7/food1.py: 131-140 GHData/Kodamayuto2001_PyTorchAlexNet/test1.py: 212-222
GHData/Kodamayuto2001_PyTorchTest05/test6.py: 126-135 GHData/Kodamayuto2001_PyTorch6/test4.py: 59-68 GHData/Kodamayuto2001_PyTorchSimpleCNN/cnn.py: 152-162

Class 1897:   2 fragments, nominal size 17 lines, similarity 83%

GHData/DoganK01_PyTorch-U-Net-From-Scratch/model.py: 30-54 GHData/pr1266_UNet-PyTorch/model.py: 35-56

Class 1898:   2 fragments, nominal size 16 lines, similarity 100%

GHData/DoganK01_PyTorch-U-Net-From-Scratch/model.py: 55-77 GHData/pr1266_UNet-PyTorch/model.py: 57-79

Class 1899:   2 fragments, nominal size 21 lines, similarity 78%

GHData/DoganK01_PyTorch-U-Net-From-Scratch/utils.py: 64-88 GHData/vence-andersen_UNet-PyTorch/utils.py: 77-104

Class 1900:   2 fragments, nominal size 11 lines, similarity 100%

GHData/DoganK01_PyTorch-U-Net-From-Scratch/utils.py: 89-104 GHData/DoganK01_PyTorch-U-Net-From-Scratch/get_mask.py: 5-19

Class 1901:   2 fragments, nominal size 13 lines, similarity 100%

GHData/DoganK01_PyTorch-U-Net-From-Scratch/utils.py: 105-119 GHData/vence-andersen_UNet-PyTorch/utils.py: 105-119

Class 1902:   3 fragments, nominal size 11 lines, similarity 81%

GHData/DoganK01_PyTorch-U-Net-From-Scratch/dataset.py: 25-37 GHData/baroibeo_UNet-PyTorch-Implementation-on-Carvana-Image-Masking-Challenge-dataset/dataset.py: 16-27 GHData/vence-andersen_UNet-PyTorch/dataset.py: 16-28

Class 1903:   2 fragments, nominal size 10 lines, similarity 70%

GHData/DashankaNadeeshanDeSilva_Multilayer_Perceptron_with_PyTorch/Multilayer_Perceptron.py: 70-84 GHData/DashankaNadeeshanDeSilva_Multilayer_Perceptron_with_PyTorch/Multilayer_Perceptron.py: 459-473

Class 1904:   2 fragments, nominal size 15 lines, similarity 75%

GHData/DashankaNadeeshanDeSilva_Multilayer_Perceptron_with_PyTorch/Multilayer_Perceptron.py: 202-229 GHData/DashankaNadeeshanDeSilva_Multilayer_Perceptron_with_PyTorch/Multilayer_Perceptron.py: 239-262

Class 1905:   2 fragments, nominal size 17 lines, similarity 83%

GHData/DashankaNadeeshanDeSilva_Multilayer_Perceptron_with_PyTorch/Multilayer_Perceptron.py: 306-333 GHData/DashankaNadeeshanDeSilva_Multilayer_Perceptron_with_PyTorch/Multilayer_Perceptron.py: 392-416

Class 1906:   3 fragments, nominal size 32 lines, similarity 96%

GHData/hwangtamu_TorchSequential/test_encoder.py: 57-97 GHData/hwangtamu_TorchSequential/test.py: 59-99 GHData/hwangtamu_TorchSequential/vis_state.py: 58-98

Class 1907:   3 fragments, nominal size 22 lines, similarity 100%

GHData/hwangtamu_TorchSequential/test_encoder.py: 98-124 GHData/hwangtamu_TorchSequential/vis_state.py: 99-125 GHData/hwangtamu_TorchSequential/test.py: 100-127

Class 1908:   2 fragments, nominal size 15 lines, similarity 100%

GHData/hwangtamu_TorchSequential/dna_test.py: 12-27 GHData/hwangtamu_TorchSequential/dna_pruning.py: 24-39

Class 1909:   2 fragments, nominal size 39 lines, similarity 100%

GHData/hwangtamu_TorchSequential/dna_test.py: 28-81 GHData/hwangtamu_TorchSequential/dna_pruning.py: 40-93

Class 1910:   3 fragments, nominal size 34 lines, similarity 71%

GHData/hwangtamu_TorchSequential/dna_pruning.py: 173-212 GHData/hwangtamu_TorchSequential/prunning.py: 106-149 GHData/hwangtamu_TorchSequential/wasser.py: 119-163

Class 1911:   2 fragments, nominal size 53 lines, similarity 100%

GHData/hwangtamu_TorchSequential/prunning.py: 24-105 GHData/hwangtamu_TorchSequential/wasser.py: 36-118

Class 1912:   2 fragments, nominal size 17 lines, similarity 88%

GHData/MogicianXD_CML_torch/BaseModel.py: 90-107 GHData/MogicianXD_CML_torch/BaseModel.py: 108-125

Class 1913:   3 fragments, nominal size 11 lines, similarity 72%

GHData/yjh0410_new-YOLOv1_PyTorch/tools.py: 13-25 GHData/dreamplus1989_yolov3-plus_PyTorch/tools.py: 16-28 GHData/dreamplus1989_yolov3-plus_PyTorch/tools.py: 33-46

Class 1914:   3 fragments, nominal size 27 lines, similarity 70%

GHData/yjh0410_new-YOLOv1_PyTorch/tools.py: 86-123 GHData/dreamplus1989_yolov3-plus_PyTorch/tools.py: 287-332 GHData/dreamplus1989_yolov3-plus_PyTorch/tools.py: 333-372

Class 1915:   3 fragments, nominal size 16 lines, similarity 100%

GHData/polasha_Neural-network_PyTorch_Census_Income_Dataset/Neural%20Network%20Exercise_census%20income%20dataset.py: 124-151 GHData/polasha_Artificial-Neural-Network_tabular-dataset_PyTorch/Artificial%20Neural%20Network_Tabular%20dataset.py: 136-155 GHData/polasha_Artificial-Neural-Network-with-tabular-dataset_PyTorch-consider-fareclass_lebel-/Artificial%20neural%20network_Tabular%20data(Fare_class).py: 134-153

Class 1916:   3 fragments, nominal size 10 lines, similarity 100%

GHData/polasha_Neural-network_PyTorch_Census_Income_Dataset/Neural%20Network%20Exercise_census%20income%20dataset.py: 152-169 GHData/polasha_Artificial-Neural-Network-with-tabular-dataset_PyTorch-consider-fareclass_lebel-/Artificial%20neural%20network_Tabular%20data(Fare_class).py: 154-166 GHData/polasha_Artificial-Neural-Network_tabular-dataset_PyTorch/Artificial%20Neural%20Network_Tabular%20dataset.py: 156-168

Class 1917:   6 fragments, nominal size 27 lines, similarity 70%

GHData/eugenelet_PyTorch-Transfer-Learning-of-VGG19-for-Cifar-10-Dataset/test_vgg19.py: 116-150 GHData/jonnedtc_U-Net-PyTorch/networks.py: 447-480 GHData/LizzieTang_PyTorch-YOLOv1-ongoing/yolonet.py: 91-138
GHData/tensorinfinitysip_a-PyTorch-Project-to-SSD/model.py: 51-88 GHData/PrimadonnaGit_SSD-Detector-PyTorch/model.py: 51-89 GHData/jonnedtc_U-Net-PyTorch/networks.py: 52-100

Class 1918:   2 fragments, nominal size 18 lines, similarity 100%

GHData/IFADA_TorchFace/detesetTest.py: 9-32 GHData/IFADA_TorchFace/deteset.py: 13-36

Class 1919:   2 fragments, nominal size 23 lines, similarity 87%

GHData/IFADA_TorchFace/detesetTest.py: 39-65 GHData/IFADA_TorchFace/deteset.py: 57-93

Class 1920:   2 fragments, nominal size 17 lines, similarity 83%

GHData/IFADA_TorchFace/nets.py: 31-48 GHData/IFADA_TorchFace/nets.py: 62-83

Class 1921:   2 fragments, nominal size 34 lines, similarity 97%

GHData/IFADA_TorchFace/deteset.py: 94-134 GHData/IFADA_TorchFace/deteset.py: 135-178

Class 1922:   2 fragments, nominal size 11 lines, similarity 100%

GHData/jjgarau_COMPSsTorch/kmeans_torchcompss.py: 12-24 GHData/jjgarau_COMPSsTorch/kmeans_torchcompss_numpy.py: 12-24

Class 1923:   2 fragments, nominal size 14 lines, similarity 80%

GHData/jjgarau_COMPSsTorch/kmeans_torchcompss.py: 26-40 GHData/jjgarau_COMPSsTorch/kmeans_torchcompss_numpy.py: 26-42

Class 1924:   2 fragments, nominal size 15 lines, similarity 86%

GHData/jjgarau_COMPSsTorch/kmeans_torchcompss.py: 66-86 GHData/jjgarau_COMPSsTorch/kmeans_torchcompss_numpy.py: 64-83

Class 1925:   2 fragments, nominal size 12 lines, similarity 100%

GHData/Lluvia-Tang_torch_base/eval.py: 25-42 GHData/nuaazs_torch_cookbook/eval.py: 24-41

Class 1926:   3 fragments, nominal size 25 lines, similarity 75%

GHData/Lluvia-Tang_torch_base/train.py: 141-182 GHData/Lluvia-Tang_torch_base/twoSteps_Train.py: 169-204 GHData/Lluvia-Tang_torch_base/graph_train.py: 110-139

Class 1927:   2 fragments, nominal size 12 lines, similarity 100%

GHData/Lluvia-Tang_torch_base/options.py: 5-18 GHData/nuaazs_torch_cookbook/options.py: 5-18

Class 1928:   2 fragments, nominal size 15 lines, similarity 81%

GHData/Lluvia-Tang_torch_base/options.py: 19-42 GHData/nuaazs_torch_cookbook/options.py: 19-34

Class 1929:   2 fragments, nominal size 10 lines, similarity 100%

GHData/hanzlfs_SoundNet_PyTorch/main_train.py: 44-56 GHData/hanzlfs_SoundNet_PyTorch/main_train_small.py: 46-58

Class 1930:   2 fragments, nominal size 27 lines, similarity 70%

GHData/hanzlfs_SoundNet_PyTorch/main_train.py: 57-98 GHData/hanzlfs_SoundNet_PyTorch/main_train_small.py: 59-91

Class 1931:   2 fragments, nominal size 16 lines, similarity 100%

GHData/LongLong-Jing_PyTorch-UNet/loss.py: 13-35 GHData/zack-yu666_PyTorch-deeplabv2/loss.py: 15-39

Class 1932:   2 fragments, nominal size 30 lines, similarity 100%

GHData/rahul-jha98_YOLOv4-PyTorch/train.py: 116-151 GHData/Fuyi-Yang_Package-Detection-PyTorch-YOLOv4/train.py: 89-124

Class 1933:   2 fragments, nominal size 52 lines, similarity 98%

GHData/rahul-jha98_YOLOv4-PyTorch/train.py: 152-219 GHData/Fuyi-Yang_Package-Detection-PyTorch-YOLOv4/train.py: 125-189

Class 1934:   2 fragments, nominal size 29 lines, similarity 82%

GHData/rahul-jha98_YOLOv4-PyTorch/train.py: 220-261 GHData/Fuyi-Yang_Package-Detection-PyTorch-YOLOv4/train.py: 190-231

Class 1935:   2 fragments, nominal size 12 lines, similarity 100%

GHData/rahul-jha98_YOLOv4-PyTorch/train.py: 262-275 GHData/Fuyi-Yang_Package-Detection-PyTorch-YOLOv4/train.py: 232-245

Class 1936:   2 fragments, nominal size 10 lines, similarity 100%

GHData/rahul-jha98_YOLOv4-PyTorch/train.py: 320-330 GHData/Fuyi-Yang_Package-Detection-PyTorch-YOLOv4/train.py: 285-295

Class 1937:   2 fragments, nominal size 25 lines, similarity 96%

GHData/rahul-jha98_YOLOv4-PyTorch/train.py: 537-566 GHData/Fuyi-Yang_Package-Detection-PyTorch-YOLOv4/train.py: 370-405

Class 1938:   2 fragments, nominal size 26 lines, similarity 92%

GHData/MaxandYuki_R21D_basic_PyTorch/train_val.py: 54-93 GHData/BannyStone_Video_Classification_PyTorch/train_val.py: 216-257

Class 1939:   5 fragments, nominal size 100 lines, similarity 85%

GHData/MaxandYuki_R21D_basic_PyTorch/main_21d.py: 18-143 GHData/BannyStone_Video_Classification_PyTorch/main.py: 24-167 GHData/BannyStone_Video_Classification_PyTorch/main_20bn.py: 24-153 GHData/BannyStone_Video_Classification_PyTorch/finetune_bn_frozen.py: 24-159 GHData/BannyStone_Video_Classification_PyTorch/finetune_fc.py: 23-153

Class 1940:   3 fragments, nominal size 89 lines, similarity 71%

GHData/MaxandYuki_R21D_basic_PyTorch/test_v1.py: 37-149 GHData/BannyStone_Video_Classification_PyTorch/test_kaiming.py: 68-180 GHData/BannyStone_Video_Classification_PyTorch/test_10crop.py: 40-136

Class 1941:   2 fragments, nominal size 10 lines, similarity 100%

GHData/MlWoo_WaveRNN-PyTorch/infolog.py: 14-25 GHData/MlWoo_Tacotron2-PyTorch/infolog.py: 14-25

Class 1942:   2 fragments, nominal size 12 lines, similarity 84%

GHData/justusschock_torch_layers/setup.py: 7-19 GHData/delira-dev_vision_torch/setup.py: 5-19

Class 1943:   2 fragments, nominal size 15 lines, similarity 100%

GHData/tabularasa066a_StyleGAN_PyTorch/stylegan_layers.py: 12-28 GHData/ndb796_PyTorch-StyleGAN-Face-Editting/stylegan_model.py: 10-25

Class 1944:   2 fragments, nominal size 24 lines, similarity 76%

GHData/tabularasa066a_StyleGAN_PyTorch/stylegan_layers.py: 37-63 GHData/ndb796_PyTorch-StyleGAN-Face-Editting/stylegan_model.py: 34-58

Class 1945:   2 fragments, nominal size 12 lines, similarity 91%

GHData/tabularasa066a_StyleGAN_PyTorch/stylegan_layers.py: 150-161 GHData/ndb796_PyTorch-StyleGAN-Face-Editting/stylegan_model.py: 125-137

Class 1946:   2 fragments, nominal size 22 lines, similarity 100%

GHData/tabularasa066a_StyleGAN_PyTorch/stylegan_layers.py: 195-218 GHData/ndb796_PyTorch-StyleGAN-Face-Editting/stylegan_model.py: 166-189

Class 1947:   2 fragments, nominal size 15 lines, similarity 100%

GHData/tabularasa066a_StyleGAN_PyTorch/stylegan_layers.py: 239-253 GHData/ndb796_PyTorch-StyleGAN-Face-Editting/stylegan_model.py: 210-225

Class 1948:   2 fragments, nominal size 12 lines, similarity 100%

GHData/tabularasa066a_StyleGAN_PyTorch/stylegan_layers.py: 264-277 GHData/ndb796_PyTorch-StyleGAN-Face-Editting/stylegan_model.py: 236-248

Class 1949:   2 fragments, nominal size 11 lines, similarity 100%

GHData/tabularasa066a_StyleGAN_PyTorch/stylegan_layers.py: 278-290 GHData/ndb796_PyTorch-StyleGAN-Face-Editting/stylegan_model.py: 249-261

Class 1950:   2 fragments, nominal size 11 lines, similarity 81%

GHData/tabularasa066a_StyleGAN_PyTorch/stylegan_layers.py: 292-304 GHData/ndb796_PyTorch-StyleGAN-Face-Editting/stylegan_model.py: 263-273

Class 1951:   2 fragments, nominal size 46 lines, similarity 78%

GHData/tabularasa066a_StyleGAN_PyTorch/stylegan_layers.py: 314-370 GHData/ndb796_PyTorch-StyleGAN-Face-Editting/stylegan_model.py: 283-327

Class 1952:   2 fragments, nominal size 12 lines, similarity 91%

GHData/bnusss_SRGAN-PyTorch/data.py: 79-92 GHData/bnusss_SRGAN-PyTorch/data.py: 93-106

Class 1953:   2 fragments, nominal size 32 lines, similarity 71%

GHData/tristandb_EfficientDet-PyTorch/retinanet.py: 229-279 GHData/tristandb_EfficientDet-PyTorch/efficientdet.py: 148-197

Class 1954:   4 fragments, nominal size 11 lines, similarity 100%

GHData/linjx-ustc1106_DosGAN-PyTorch/solver_dosgan.py: 129-145 GHData/linjx-ustc1106_ZstGAN-PyTorch/trainer.py: 156-169 GHData/bottlecapper_StarGAN-PyTorch/solver.py: 128-141 GHData/ZhenyueQin_Implementation-MolGAN-PyTorch/solver_gan.py: 133-146

Class 1955:   2 fragments, nominal size 108 lines, similarity 82%

GHData/linjx-ustc1106_DosGAN-PyTorch/solver_dosgan.py: 151-336 GHData/linjx-ustc1106_DosGAN-PyTorch/solver_dosgan.py: 337-513

Class 1956:   3 fragments, nominal size 14 lines, similarity 92%

GHData/linjx-ustc1106_DosGAN-PyTorch/model.py: 130-146 GHData/linjx-ustc1106_ZstGAN-PyTorch/model.py: 466-483 GHData/bottlecapper_StarGAN-PyTorch/model.py: 65-81

Class 1957:   2 fragments, nominal size 38 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/engine.py: 20-83 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/engine.py: 20-83

Class 1958:   2 fragments, nominal size 51 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/engine.py: 84-156 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/engine.py: 84-156

Class 1959:   2 fragments, nominal size 40 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/engine.py: 157-210 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/engine.py: 157-210

Class 1960:   2 fragments, nominal size 12 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/model.py: 38-53 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/model.py: 38-53

Class 1961:   2 fragments, nominal size 10 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/model.py: 54-77 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/model.py: 54-77

Class 1962:   2 fragments, nominal size 29 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/model.py: 121-167 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/model.py: 121-167

Class 1963:   2 fragments, nominal size 18 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/model.py: 173-193 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/model.py: 173-193

Class 1964:   2 fragments, nominal size 10 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/model.py: 194-207 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/model.py: 194-207

Class 1965:   2 fragments, nominal size 88 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/train.py: 30-151 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/train.py: 30-151

Class 1966:   2 fragments, nominal size 16 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/train.py: 152-171 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/train.py: 152-171

Class 1967:   2 fragments, nominal size 10 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/utils.py: 106-128 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/utils.py: 106-128

Class 1968:   2 fragments, nominal size 15 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/utils.py: 129-154 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/utils.py: 129-154

Class 1969:   2 fragments, nominal size 10 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/utils.py: 155-166 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/utils.py: 155-166

Class 1970:   6 fragments, nominal size 20 lines, similarity 80%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/utils.py: 167-203 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/rerank.py: 70-100 GHData/Wangt-CN_MTFN-RR-PyTorch-Code/utils.py: 257-285
GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/utils.py: 167-203 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/utils.py: 257-285 GHData/Wangt-CN_MTFN-RR-PyTorch-Code/rerank.py: 70-100

Class 1971:   6 fragments, nominal size 16 lines, similarity 93%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/utils.py: 204-230 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/utils.py: 204-230 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/rerank.py: 101-128
GHData/Wangt-CN_MTFN-RR-PyTorch-Code/utils.py: 286-309 GHData/Wangt-CN_MTFN-RR-PyTorch-Code/rerank.py: 101-128 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/utils.py: 286-309

Class 1972:   2 fragments, nominal size 16 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/utils.py: 231-256 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/utils.py: 231-256

Class 1973:   2 fragments, nominal size 17 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/rerank.py: 15-39 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/rerank.py: 15-39

Class 1974:   2 fragments, nominal size 22 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/rerank.py: 40-69 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/rerank.py: 40-69

Class 1975:   2 fragments, nominal size 14 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/data.py: 21-43 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/data.py: 21-43

Class 1976:   2 fragments, nominal size 15 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/data.py: 44-63 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/data.py: 44-63

Class 1977:   2 fragments, nominal size 10 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/data.py: 96-109 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/data.py: 96-109

Class 1978:   2 fragments, nominal size 21 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/vocab.py: 70-103 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/vocab.py: 70-103

Class 1979:   2 fragments, nominal size 10 lines, similarity 100%

GHData/Wangt-CN_MTFN-RR-PyTorch-Code/seq2vec.py: 15-25 GHData/Riadh-B_MTFN-RR-PyTorch-Code-master/seq2vec.py: 15-25

Class 1980:   2 fragments, nominal size 20 lines, similarity 100%

GHData/SJ-Chuang_PyTorch-KPN/eval.py: 13-37 GHData/SJ-Chuang_PyTorch-KPN/train.py: 13-37

Class 1981:   2 fragments, nominal size 14 lines, similarity 100%

GHData/AMITKESARI2000_torchhub-isl/hubconf.py: 50-71 GHData/nmnsharma007_isl_torch_hub/hubconf.py: 47-68

Class 1982:   2 fragments, nominal size 17 lines, similarity 100%

GHData/vivanov879_ner_window/collectSubmission.py: 28-52 GHData/vivanov879_lstm_language_model/collectSubmission.py: 28-52

Class 1983:   2 fragments, nominal size 11 lines, similarity 100%

GHData/vivanov879_ner_window/collectSubmission.py: 68-79 GHData/vivanov879_lstm_language_model/collectSubmission.py: 68-79

Class 1984:   2 fragments, nominal size 10 lines, similarity 100%

GHData/vivanov879_ner_window/collectSubmission.py: 81-91 GHData/vivanov879_lstm_language_model/collectSubmission.py: 81-91

Class 1985:   2 fragments, nominal size 12 lines, similarity 100%

GHData/vivanov879_ner_window/collectSubmission.py: 102-115 GHData/vivanov879_lstm_language_model/collectSubmission.py: 102-115

Class 1986:   2 fragments, nominal size 13 lines, similarity 100%

GHData/vivanov879_ner_window/collectSubmission.py: 117-131 GHData/vivanov879_lstm_language_model/collectSubmission.py: 117-131

Class 1987:   2 fragments, nominal size 13 lines, similarity 100%

GHData/vivanov879_ner_window/collectSubmission.py: 166-179 GHData/vivanov879_lstm_language_model/collectSubmission.py: 166-179

Class 1988:   2 fragments, nominal size 12 lines, similarity 100%

GHData/vivanov879_ner_window/collectSubmission.py: 181-194 GHData/vivanov879_lstm_language_model/collectSubmission.py: 181-194

Class 1989:   2 fragments, nominal size 14 lines, similarity 100%

GHData/vivanov879_ner_window/nerwindow.py: 30-75 GHData/vivanov879_lstm_language_model/nerwindow.py: 30-75

Class 1990:   2 fragments, nominal size 12 lines, similarity 100%

GHData/vivanov879_ner_window/softmax_example.py: 12-47 GHData/vivanov879_lstm_language_model/softmax_example.py: 12-47

Class 1991:   6 fragments, nominal size 11 lines, similarity 72%

GHData/zhaort_torch_test/WGAN.py: 19-30 GHData/igeng_Swaggy_pytorch/L57_gan.py: 35-48 GHData/igeng_Swaggy_pytorch/L58_wgan_gp.py: 15-27
GHData/igeng_Swaggy_pytorch/L58_wgan_gp.py: 35-48 GHData/igeng_Swaggy_pytorch/L57_gan.py: 15-27 GHData/zhaort_torch_test/WGAN.py: 37-49

Class 1992:   3 fragments, nominal size 12 lines, similarity 91%

GHData/zhaort_torch_test/WGAN.py: 73-87 GHData/zhaort_torch_test/MyGAN1.py: 59-72 GHData/zhaort_torch_test/MyGAN2.py: 117-129

Class 1993:   2 fragments, nominal size 17 lines, similarity 88%

GHData/zhaort_torch_test/MyGAN2.py: 42-60 GHData/zhaort_torch_test/GAN2.py: 38-56

Class 1994:   3 fragments, nominal size 16 lines, similarity 70%

GHData/zhaort_torch_test/MyGAN2.py: 70-91 GHData/zhaort_torch_test/GAN2.py: 66-87 GHData/Mark-Sang_PyTorchGanMNIST-GPU/test.py: 61-80

Class 1995:   2 fragments, nominal size 11 lines, similarity 90%

GHData/SonOfGod777_SoftMaskBert-torch/train.py: 98-109 GHData/SonOfGod777_SoftMaskBert-torch/train.py: 110-122

Class 1996:   2 fragments, nominal size 15 lines, similarity 100%

GHData/Normalist-K_torch_lightning_base/run.py: 12-36 GHData/Normalist-K_torch_lightning_base/infer.py: 12-36

Class 1997:   2 fragments, nominal size 34 lines, similarity 100%

GHData/hz-ants_PoseCNN_PyTorch/eval_net.py: 17-58 GHData/nxu96_PoseCNN_PyTorch/eval_net.py: 17-58

Class 1998:   2 fragments, nominal size 39 lines, similarity 100%

GHData/hz-ants_PoseCNN_PyTorch/train_net.py: 41-92 GHData/nxu96_PoseCNN_PyTorch/train_net.py: 41-92

Class 1999:   2 fragments, nominal size 20 lines, similarity 100%

GHData/hz-ants_PoseCNN_PyTorch/train_net.py: 112-136 GHData/nxu96_PoseCNN_PyTorch/train_net.py: 112-136

Class 2000:   2 fragments, nominal size 26 lines, similarity 100%

GHData/hz-ants_PoseCNN_PyTorch/train_net.py: 137-165 GHData/nxu96_PoseCNN_PyTorch/train_net.py: 137-165

Class 2001:   2 fragments, nominal size 59 lines, similarity 100%

GHData/hz-ants_PoseCNN_PyTorch/train_net.py: 166-233 GHData/nxu96_PoseCNN_PyTorch/train_net.py: 166-233

Class 2002:   2 fragments, nominal size 44 lines, similarity 100%

GHData/hz-ants_PoseCNN_PyTorch/train_net.py: 234-289 GHData/nxu96_PoseCNN_PyTorch/train_net.py: 234-289

Class 2003:   2 fragments, nominal size 12 lines, similarity 100%

GHData/hz-ants_PoseCNN_PyTorch/train_net.py: 290-303 GHData/nxu96_PoseCNN_PyTorch/train_net.py: 290-303

Class 2004:   2 fragments, nominal size 139 lines, similarity 100%

GHData/hz-ants_PoseCNN_PyTorch/train_net.py: 310-501 GHData/nxu96_PoseCNN_PyTorch/train_net.py: 310-501

Class 2005:   2 fragments, nominal size 19 lines, similarity 100%

GHData/ImKeTT_WAEs-torch/WAE_mmd_img.py: 68-89 GHData/ImKeTT_WAEs-torch/WAE_adv_img.py: 63-84

Class 2006:   2 fragments, nominal size 17 lines, similarity 100%

GHData/ImKeTT_WAEs-torch/WAE_mmd_img.py: 98-120 GHData/ImKeTT_WAEs-torch/WAE_adv_img.py: 92-114

Class 2007:   2 fragments, nominal size 26 lines, similarity 80%

GHData/ImKeTT_WAEs-torch/WAE_mmd_img.py: 128-162 GHData/ImKeTT_WAEs-torch/WAE_mmd_img.py: 163-197

Class 2008:   2 fragments, nominal size 17 lines, similarity 77%

GHData/sagnik1511_Pix2PixGAN-PyTorch/disc_model.py: 19-39 GHData/phranq0_pix2pix_torch/discriminator.py: 24-49

Class 2009:   5 fragments, nominal size 34 lines, similarity 74%

GHData/Ikomia-hub_train_torchvision_mask_rcnn/train_torchvision_mask_rcnn_widget.py: 14-59 GHData/Ikomia-hub_train_torchvision_resnext/train_torchvision_resnext_widget.py: 14-67 GHData/Ikomia-hub_train_torchvision_resnet/train_torchvision_resnet_widget.py: 14-68 GHData/Ikomia-hub_train_torchvision_faster_rcnn/train_torchvision_faster_rcnn_widget.py: 14-56 GHData/Ikomia-hub_train_torchvision_mnasnet/train_torchvision_mnasnet_widget.py: 14-65

Class 2010:   4 fragments, nominal size 11 lines, similarity 83%

GHData/Ikomia-hub_train_torchvision_mask_rcnn/train_torchvision_mask_rcnn_widget.py: 60-79 GHData/Ikomia-hub_train_torchvision_resnet/train_torchvision_resnet_widget.py: 69-90 GHData/Ikomia-hub_train_torchvision_resnext/train_torchvision_resnext_widget.py: 68-89 GHData/Ikomia-hub_train_torchvision_mnasnet/train_torchvision_mnasnet_widget.py: 66-86

Class 2011:   2 fragments, nominal size 16 lines, similarity 100%

GHData/Ikomia-hub_train_torchvision_mask_rcnn/trainer.py: 29-51 GHData/Ikomia-hub_train_torchvision_faster_rcnn/faster_rcnn.py: 29-51

Class 2012:   2 fragments, nominal size 20 lines, similarity 100%

GHData/Ikomia-hub_train_torchvision_mask_rcnn/trainer.py: 59-90 GHData/Ikomia-hub_train_torchvision_faster_rcnn/faster_rcnn.py: 59-90

Class 2013:   2 fragments, nominal size 27 lines, similarity 100%

GHData/Ikomia-hub_train_torchvision_mask_rcnn/trainer.py: 91-136 GHData/Ikomia-hub_train_torchvision_faster_rcnn/faster_rcnn.py: 91-136

Class 2014:   2 fragments, nominal size 25 lines, similarity 84%

GHData/Windxy_Classic_Network_PyTorch/AlexNet.py: 7-48 GHData/clw5180_PyTorch_Practice/AlexNet.py: 15-46

Class 2015:   2 fragments, nominal size 11 lines, similarity 100%

GHData/Windxy_Classic_Network_PyTorch/MobileNet.py: 8-19 GHData/Windxy_Classic_Network_PyTorch/MobileNetv2.py: 6-17

Class 2016:   2 fragments, nominal size 11 lines, similarity 81%

GHData/Windxy_Classic_Network_PyTorch/InceptionV3.py: 111-126 GHData/Windxy_Classic_Network_PyTorch/InceptionV4.py: 124-137

Class 2017:   5 fragments, nominal size 11 lines, similarity 71%

GHData/Windxy_Classic_Network_PyTorch/InceptionV3.py: 138-153 GHData/sharathmaidargi_finetune_torchvision/inception.py: 258-272 GHData/acholston_PyTorch_Exercises/Ex11-1b.py: 114-129 GHData/acholston_PyTorch_Exercises/Ex11-1b.py: 151-166 GHData/Windxy_Classic_Network_PyTorch/InceptionV4.py: 76-92

Class 2018:   2 fragments, nominal size 19 lines, similarity 100%

GHData/Windxy_Classic_Network_PyTorch/InceptionV3.py: 154-175 GHData/sharathmaidargi_finetune_torchvision/inception.py: 273-297

Class 2019:   3 fragments, nominal size 10 lines, similarity 70%

GHData/Windxy_Classic_Network_PyTorch/VGG16.py: 9-21 GHData/clw5180_PyTorch_Practice/VGGNet.py: 13-23 GHData/clw5180_PyTorch_Practice/VGGNet.py: 69-80

Class 2020:   2 fragments, nominal size 15 lines, similarity 86%

GHData/Windxy_Classic_Network_PyTorch/InceptionV4.py: 147-165 GHData/acholston_PyTorch_Exercises/Ex11-1b.py: 39-59

Class 2021:   2 fragments, nominal size 10 lines, similarity 80%

GHData/vnj64_CatOrDog_OpenCV/auth.py: 16-32 GHData/BenMueller1_Flask-PyTorch-image-classifier/index.py: 54-66

Class 2022:   2 fragments, nominal size 12 lines, similarity 75%

GHData/vnj64_CatOrDog_OpenCV/auth.py: 38-58 GHData/BenMueller1_Flask-PyTorch-image-classifier/index.py: 77-93

Class 2023:   2 fragments, nominal size 14 lines, similarity 78%

GHData/machiredd_EM_segmentation_PyTorch/utils.py: 19-34 GHData/vence-andersen_UNet-PyTorch/utils.py: 18-31

Class 2024:   2 fragments, nominal size 30 lines, similarity 80%

GHData/machiredd_EM_segmentation_PyTorch/dataset_in.py: 45-89 GHData/machiredd_EM_segmentation_PyTorch/dataset_in.py: 102-148

Class 2025:   2 fragments, nominal size 13 lines, similarity 92%

GHData/VishalBalaji321_ResNet-in-PyTorch/resnet_in_pytorch.py: 125-139 GHData/pratyush-1_Resnet-pyTorch/Resnet.py: 76-94

Class 2026:   2 fragments, nominal size 16 lines, similarity 100%

GHData/jweig0ld_VAE/vae_skeleton.py: 255-285 GHData/jweig0ld_VAE/vae_skeleton.py: 412-440

Class 2027:   2 fragments, nominal size 10 lines, similarity 100%

GHData/tigerneil_NNDL-PyTorch/network.py: 78-93 GHData/tigerneil_NNDL-PyTorch/network.py: 94-108

Class 2028:   2 fragments, nominal size 10 lines, similarity 80%

GHData/yoonsanghyu_Dual-Path-Transformer-Network-PyTorch/dptnet.py: 189-203 GHData/yoonsanghyu_FaSNet-TAC-PyTorch/FaSNet.py: 84-101

Class 2029:   2 fragments, nominal size 10 lines, similarity 100%

GHData/arynoviyanto2_torch-wrapper/performance_evaluation.py: 33-46 GHData/arynoviyanto2_torch-wrapper/performance_evaluation.py: 47-59

Class 2030:   2 fragments, nominal size 17 lines, similarity 88%

GHData/chlee1983_PyTorch_Tutorial/Batch_normalization.py: 44-66 GHData/chlee1983_PyTorch_Tutorial/Batch_norm_small_input_values.py: 44-64

Class 2031:   2 fragments, nominal size 14 lines, similarity 92%

GHData/JohnNehls_PyTorchExamples/LR_miniBatch_PyTorchWay.py: 46-67 GHData/JohnNehls_PyTorchExamples/LogReg_PyTorch.py: 59-79

Class 2032:   2 fragments, nominal size 22 lines, similarity 100%

GHData/zheng-yuwei_RankIQA.PyTorch/main.py: 26-65 GHData/zheng-yuwei_PyTorch-Image-Classification/main.py: 35-74

Class 2033:   2 fragments, nominal size 19 lines, similarity 76%

GHData/zheng-yuwei_RankIQA.PyTorch/main.py: 182-207 GHData/zheng-yuwei_PyTorch-Image-Classification/main.py: 206-236

Class 2034:   3 fragments, nominal size 29 lines, similarity 83%

GHData/vivanov879_recursive_neural_network/rnn2deep.py: 55-112 GHData/vivanov879_recursive_neural_network/rnn_changed.py: 23-81 GHData/vivanov879_recursive_neural_network/rnn.py: 43-95

Class 2035:   4 fragments, nominal size 10 lines, similarity 100%

GHData/vivanov879_recursive_neural_network/rnn2deep.py: 130-149 GHData/vivanov879_recursive_neural_network/rnn_changed.py: 92-111 GHData/vivanov879_recursive_neural_network/rntn.py: 61-80 GHData/vivanov879_recursive_neural_network/rnn.py: 126-145

Class 2036:   4 fragments, nominal size 39 lines, similarity 85%

GHData/vivanov879_recursive_neural_network/rnn2deep.py: 158-201 GHData/vivanov879_recursive_neural_network/rntn.py: 89-136 GHData/vivanov879_recursive_neural_network/rnn_changed.py: 120-163 GHData/vivanov879_recursive_neural_network/rnn.py: 154-198

Class 2037:   2 fragments, nominal size 13 lines, similarity 71%

GHData/vivanov879_recursive_neural_network/rnn.py: 21-42 GHData/vivanov879_recursive_neural_network/rntn.py: 15-38

Class 2038:   2 fragments, nominal size 74 lines, similarity 76%

GHData/ZhiwenShao_PyTorch-JAANet/util.py: 27-111 GHData/ZhiwenShao_PyTorch-JAANet/util.py: 112-212

Class 2039:   2 fragments, nominal size 17 lines, similarity 70%

GHData/ZhiwenShao_PyTorch-JAANet/util.py: 279-299 GHData/ZhiwenShao_PyTorch-JAANet/util.py: 300-321

Class 2040:   2 fragments, nominal size 74 lines, similarity 89%

GHData/ZhiwenShao_PyTorch-JAANet/test_JAAv2.py: 14-106 GHData/ZhiwenShao_PyTorch-JAANet/test_JAAv1.py: 14-103

Class 2041:   2 fragments, nominal size 40 lines, similarity 78%

GHData/supernotman_Faster-RCNN-with-torchvision/train.py: 19-62 GHData/djycn_faster-rcnn-implemented-with-torchvision/detect.py: 8-52

Class 2042:   2 fragments, nominal size 19 lines, similarity 100%

GHData/Naagar_GANs_pyTorch/model_utils.py: 8-32 GHData/Naagar_GANs_pyTorch/model_utils_0.py: 11-36

Class 2043:   2 fragments, nominal size 23 lines, similarity 100%

GHData/Naagar_GANs_pyTorch/model_utils.py: 38-68 GHData/Naagar_GANs_pyTorch/model_utils_0.py: 42-74

Class 2044:   2 fragments, nominal size 14 lines, similarity 100%

GHData/xiaywang_q-eegnet_torch/eegnet_quant.py: 109-136 GHData/xiaywang_q-eegnet_torch/eegnet.py: 127-154

Class 2045:   2 fragments, nominal size 11 lines, similarity 81%

GHData/RimDan_minesweep_torch/environment.py: 35-60 GHData/RimDan_minesweep_torch/environment.py: 61-74

Class 2046:   2 fragments, nominal size 15 lines, similarity 100%

GHData/imdsafi09_PyTorch_TensorFlow_model_converter/utils_model.py: 30-48 GHData/imdsafi09_PyTorch_TensorFlow_model_converter/script.py: 183-201

Class 2047:   2 fragments, nominal size 14 lines, similarity 100%

GHData/imdsafi09_PyTorch_TensorFlow_model_converter/utils_model.py: 49-65 GHData/imdsafi09_PyTorch_TensorFlow_model_converter/script.py: 202-216

Class 2048:   2 fragments, nominal size 44 lines, similarity 71%

GHData/Pragjnesh_PyTorch/transfer.py: 69-138 GHData/IdanAzuri_PyTorch-GAN-Experiments-Env/celeba_trainer.py: 120-187

Class 2049:   3 fragments, nominal size 16 lines, similarity 93%

GHData/chuliuT_Yolov1_PyTorch/predict_multi.py: 22-45 GHData/chuliuT_Yolov1_PyTorch/yolo_loss.py: 7-29 GHData/chuliuT_Yolov1_PyTorch/predict.py: 17-40

Class 2050:   2 fragments, nominal size 19 lines, similarity 100%

GHData/chuliuT_Yolov1_PyTorch/predict_multi.py: 46-70 GHData/chuliuT_Yolov1_PyTorch/predict.py: 41-67

Class 2051:   2 fragments, nominal size 22 lines, similarity 100%

GHData/chuliuT_Yolov1_PyTorch/predict_multi.py: 76-103 GHData/chuliuT_Yolov1_PyTorch/predict.py: 68-94

Class 2052:   2 fragments, nominal size 13 lines, similarity 76%

GHData/chuliuT_Yolov1_PyTorch/predict_multi.py: 104-124 GHData/chuliuT_Yolov1_PyTorch/predict.py: 99-119

Class 2053:   2 fragments, nominal size 23 lines, similarity 100%

GHData/matsengrp_torchdms/versioneer.py: 298-340 GHData/Xylambda_torchfitter/versioneer.py: 288-330

Class 2054:   2 fragments, nominal size 21 lines, similarity 100%

GHData/matsengrp_torchdms/versioneer.py: 341-370 GHData/Xylambda_torchfitter/versioneer.py: 331-360

Class 2055:   2 fragments, nominal size 35 lines, similarity 88%

GHData/matsengrp_torchdms/versioneer.py: 393-431 GHData/Xylambda_torchfitter/versioneer.py: 383-421

Class 2056:   2 fragments, nominal size 21 lines, similarity 100%

GHData/matsengrp_torchdms/versioneer.py: 958-985 GHData/Xylambda_torchfitter/versioneer.py: 953-980

Class 2057:   2 fragments, nominal size 42 lines, similarity 97%

GHData/matsengrp_torchdms/versioneer.py: 987-1047 GHData/Xylambda_torchfitter/versioneer.py: 982-1046

Class 2058:   2 fragments, nominal size 66 lines, similarity 89%

GHData/matsengrp_torchdms/versioneer.py: 1049-1148 GHData/Xylambda_torchfitter/versioneer.py: 1048-1156

Class 2059:   2 fragments, nominal size 31 lines, similarity 100%

GHData/matsengrp_torchdms/versioneer.py: 1149-1186 GHData/Xylambda_torchfitter/versioneer.py: 1157-1194

Class 2060:   2 fragments, nominal size 20 lines, similarity 100%

GHData/matsengrp_torchdms/versioneer.py: 1187-1217 GHData/Xylambda_torchfitter/versioneer.py: 1195-1225

Class 2061:   6 fragments, nominal size 13 lines, similarity 76%

GHData/matsengrp_torchdms/versioneer.py: 1272-1295 GHData/Xylambda_torchfitter/versioneer.py: 1353-1374 GHData/Xylambda_torchfitter/versioneer.py: 1286-1309
GHData/matsengrp_torchdms/versioneer.py: 1339-1360 GHData/matsengrp_torchdms/versioneer.py: 1312-1338 GHData/Xylambda_torchfitter/versioneer.py: 1326-1352

Class 2062:   2 fragments, nominal size 10 lines, similarity 100%

GHData/matsengrp_torchdms/versioneer.py: 1361-1380 GHData/Xylambda_torchfitter/versioneer.py: 1375-1394

Class 2063:   2 fragments, nominal size 32 lines, similarity 100%

GHData/matsengrp_torchdms/versioneer.py: 1401-1438 GHData/Xylambda_torchfitter/versioneer.py: 1415-1452

Class 2064:   2 fragments, nominal size 58 lines, similarity 100%

GHData/matsengrp_torchdms/versioneer.py: 1443-1523 GHData/Xylambda_torchfitter/versioneer.py: 1457-1537

Class 2065:   2 fragments, nominal size 119 lines, similarity 79%

GHData/matsengrp_torchdms/versioneer.py: 1529-1710 GHData/Xylambda_torchfitter/versioneer.py: 1543-1758

Class 2066:   2 fragments, nominal size 20 lines, similarity 100%

GHData/matsengrp_torchdms/versioneer.py: 1620-1642 GHData/matsengrp_torchdms/versioneer.py: 1653-1675

Class 2067:   2 fragments, nominal size 70 lines, similarity 97%

GHData/matsengrp_torchdms/versioneer.py: 1755-1843 GHData/Xylambda_torchfitter/versioneer.py: 1803-1893

Class 2068:   2 fragments, nominal size 34 lines, similarity 100%

GHData/matsengrp_torchdms/versioneer.py: 1844-1880 GHData/Xylambda_torchfitter/versioneer.py: 1894-1930

Class 2069:   2 fragments, nominal size 20 lines, similarity 100%

GHData/miraclewkf_SENet-PyTorch/se_resnet.py: 17-38 GHData/chaozhong2010_SENet-PyTorch/se_resnet.py: 17-38

Class 2070:   2 fragments, nominal size 23 lines, similarity 100%

GHData/miraclewkf_SENet-PyTorch/se_resnet.py: 71-94 GHData/chaozhong2010_SENet-PyTorch/se_resnet.py: 71-94

Class 2071:   2 fragments, nominal size 23 lines, similarity 100%

GHData/miraclewkf_SENet-PyTorch/se_resnext.py: 16-40 GHData/chaozhong2010_SENet-PyTorch/se_resnext.py: 16-40

Class 2072:   3 fragments, nominal size 39 lines, similarity 71%

GHData/ralcant_reconet_torch_copy/video_cv2.py: 25-87 GHData/ralcant_reconet_torch_copy/video_cv2.py: 88-138 GHData/liulai_reconet-torch/video_cv2.py: 12-57

Class 2073:   2 fragments, nominal size 32 lines, similarity 71%

GHData/ralcant_reconet_torch_copy/video_cv2.py: 139-181 GHData/liulai_reconet-torch/video_cv2.py: 58-94

Class 2074:   2 fragments, nominal size 18 lines, similarity 100%

GHData/ralcant_reconet_torch_copy/data_load.py: 35-60 GHData/liulai_reconet-torch/data_load.py: 34-57

Class 2075:   2 fragments, nominal size 13 lines, similarity 100%

GHData/ralcant_reconet_torch_copy/data_load.py: 61-81 GHData/liulai_reconet-torch/data_load.py: 142-163

Class 2076:   4 fragments, nominal size 16 lines, similarity 93%

GHData/ralcant_reconet_torch_copy/data_load.py: 90-115 GHData/liulai_reconet-torch/data_load.py: 197-220 GHData/ralcant_reconet_torch_copy/data_load.py: 116-140 GHData/liulai_reconet-torch/data_load.py: 172-196

Class 2077:   2 fragments, nominal size 16 lines, similarity 93%

GHData/ralcant_reconet_torch_copy/data_load.py: 141-163 GHData/liulai_reconet-torch/data_load.py: 221-242

Class 2078:   2 fragments, nominal size 74 lines, similarity 86%

GHData/ralcant_reconet_torch_copy/train.py: 218-310 GHData/liulai_reconet-torch/train.py: 189-290

Class 2079:   2 fragments, nominal size 16 lines, similarity 100%

GHData/ralcant_reconet_torch_copy/network.py: 73-93 GHData/liulai_reconet-torch/network.py: 73-93

Class 2080:   2 fragments, nominal size 15 lines, similarity 100%

GHData/ralcant_reconet_torch_copy/network.py: 166-182 GHData/liulai_reconet-torch/network.py: 166-182

Class 2081:   2 fragments, nominal size 17 lines, similarity 100%

GHData/ralcant_reconet_torch_copy/python_pfm.py: 137-160 GHData/liulai_reconet-torch/python_pfm.py: 137-160

Class 2082:   2 fragments, nominal size 19 lines, similarity 100%

GHData/ralcant_reconet_torch_copy/python_pfm.py: 161-184 GHData/liulai_reconet-torch/python_pfm.py: 161-184

Class 2083:   2 fragments, nominal size 13 lines, similarity 100%

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GHData/foamliu_Image-Captioning-PyTorch/models.py: 91-120 GHData/wendywlh_pyTorch_image_captioning/models.py: 94-123 GHData/sgrvinod_a-PyTorch-Tutorial-to-Image-Captioning/models.py: 94-123

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GHData/OctopusLi_a-PyTorch-Tutorial-to-Image-Captioning/models.py: 161-214 GHData/foamliu_Image-Captioning-PyTorch/models.py: 155-207 GHData/zheyejs_a-PyTorch-Image-Captioning/models.py: 161-214

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GHData/bliunlpr_gan_torch/model.py: 373-394 GHData/bliunlpr_gan_torch/model.py: 403-424

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GHData/yueqiw_OptML-SVRG-PyTorch/run.py: 87-103 GHData/yueqiw_OptML-SVRG-PyTorch/run.py: 104-120 GHData/yueqiw_OptML-SVRG-PyTorch/run.py: 173-189 GHData/yueqiw_OptML-SVRG-PyTorch/run.py: 156-172 GHData/yueqiw_OptML-SVRG-PyTorch/run.py: 138-155

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GHData/andrew-bydlon_PyTorch/MNISTconv.py: 63-91 GHData/Elain-q_MNIST-classification-with-PyTorch/MNIST%20with%20%20PyTorch.py: 33-62 GHData/andrew-bydlon_PyTorch/IMDB.Embeddings.py: 64-96 GHData/andrew-bydlon_PyTorch/CatDogVGG.py: 154-188 GHData/andrew-bydlon_PyTorch/IMDB.1D.Conv.py: 62-94
GHData/kumarvis_CatVsDog_Classifier_PyTorch/cat_dog_classifier_train_frmfolder01.py: 88-120

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GHData/xChiri_PyTorchBlitz/ImageTransforms.py: 18-38 GHData/yuquanle_PyTorchStudy/DataLoadProcess.py: 97-119 GHData/qinhaihong-red_TorchDaily/data_loading_tutorial.py: 236-258 GHData/Xiaoctw_praTorch/file10.py: 90-106
GHData/rohitvk1_Facial-Keypoint-Detection-with-PyTorch/data_preprocess.py: 78-96

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GHData/aqbewtra_Multi-Class-Aerial-Segmentation/train.py: 143-167 GHData/aqbewtra_Multi-Class-Aerial-Segmentation/train_bce.py: 143-167

Class 2559:   7 fragments, nominal size 11 lines, similarity 72%

GHData/yoonsanghyu_AAE-PyTorch/aae_basic.py: 49-63 GHData/yoonsanghyu_AAE-PyTorch/aae_semi_supervised.py: 47-61 GHData/yoonsanghyu_AAE-PyTorch/aae_semi_supervised.py: 72-84
GHData/yoonsanghyu_AAE-PyTorch/aae_basic.py: 74-86 GHData/yoonsanghyu_AAE-PyTorch/aae_semi_supervised.py: 91-102 GHData/yoonsanghyu_AAE-PyTorch/aae_basic.py: 94-105
GHData/yoonsanghyu_AAE-PyTorch/aae_semi_supervised.py: 108-119

Class 2560:   2 fragments, nominal size 20 lines, similarity 100%

GHData/grantsrb_PyTorch-A2C/test_env.py: 7-28 GHData/grantsrb_PyTorch-PPO/test_env.py: 7-28

Class 2561:   2 fragments, nominal size 11 lines, similarity 90%

GHData/EthanJYK_Transformer_PyTorch/Preprocessing.py: 89-105 GHData/cyk1337_Transformer-in-PyTorch/train.py: 80-91

Class 2562:   3 fragments, nominal size 10 lines, similarity 80%

GHData/david990917_Knowledge-Distillation-PyTorch/small.py: 22-32 GHData/david990917_Knowledge-Distillation-PyTorch/small.py: 42-53 GHData/david990917_Knowledge-Distillation-PyTorch/small.py: 63-74

Class 2563:   2 fragments, nominal size 10 lines, similarity 100%

GHData/sdecoder_ESRGAN-PyTorch/model.py: 37-50 GHData/xiaokai11_ESRGAN-PyTorch/model.py: 37-50

Class 2564:   2 fragments, nominal size 18 lines, similarity 100%

GHData/sdecoder_ESRGAN-PyTorch/model.py: 155-188 GHData/xiaokai11_ESRGAN-PyTorch/model.py: 155-188

Class 2565:   2 fragments, nominal size 22 lines, similarity 95%

GHData/NitzanHod_pong_dqn/ram.py: 20-48 GHData/NitzanHod_pong_dqn/main.py: 20-48

Class 2566:   2 fragments, nominal size 13 lines, similarity 73%

GHData/lorrywu_learning_mine/Conv_Rnn_Classifier.py: 11-31 GHData/lorrywu_learning_mine/LSTMClassifier.py: 11-26

Class 2567:   2 fragments, nominal size 28 lines, similarity 100%

GHData/The-RunningSnail_FactorGCN-PyTorch/train_gin.py: 23-61 GHData/ihollywhy_FactorGCN.PyTorch/train_gin.py: 22-60

Class 2568:   2 fragments, nominal size 70 lines, similarity 100%

GHData/The-RunningSnail_FactorGCN-PyTorch/train_gin.py: 62-164 GHData/ihollywhy_FactorGCN.PyTorch/train_gin.py: 61-163

Class 2569:   4 fragments, nominal size 22 lines, similarity 90%

GHData/The-RunningSnail_FactorGCN-PyTorch/train_zinc.py: 21-48 GHData/The-RunningSnail_FactorGCN-PyTorch/train_pattern.py: 62-87 GHData/ihollywhy_FactorGCN.PyTorch/train_zinc.py: 21-48 GHData/ihollywhy_FactorGCN.PyTorch/train_pattern.py: 62-87

Class 2570:   4 fragments, nominal size 79 lines, similarity 86%

GHData/The-RunningSnail_FactorGCN-PyTorch/train_zinc.py: 49-152 GHData/ihollywhy_FactorGCN.PyTorch/train_pattern.py: 88-195 GHData/The-RunningSnail_FactorGCN-PyTorch/train_pattern.py: 88-195 GHData/ihollywhy_FactorGCN.PyTorch/train_zinc.py: 49-152

Class 2571:   2 fragments, nominal size 16 lines, similarity 100%

GHData/The-RunningSnail_FactorGCN-PyTorch/train_synth.py: 20-37 GHData/ihollywhy_FactorGCN.PyTorch/train_synth.py: 20-37

Class 2572:   2 fragments, nominal size 62 lines, similarity 100%

GHData/The-RunningSnail_FactorGCN-PyTorch/train_synth.py: 46-132 GHData/ihollywhy_FactorGCN.PyTorch/train_synth.py: 46-132

Class 2573:   2 fragments, nominal size 11 lines, similarity 100%

GHData/The-RunningSnail_FactorGCN-PyTorch/train_pattern.py: 24-41 GHData/ihollywhy_FactorGCN.PyTorch/train_pattern.py: 24-41

Class 2574:   2 fragments, nominal size 18 lines, similarity 100%

GHData/The-RunningSnail_FactorGCN-PyTorch/train_pattern.py: 42-61 GHData/ihollywhy_FactorGCN.PyTorch/train_pattern.py: 42-61

Class 2575:   4 fragments, nominal size 12 lines, similarity 84%

GHData/MIV-Group_SSRDEFNet-PyTorch/metric.py: 17-33 GHData/MIVRC_SSRDEFNet-PyTorch/metric.py: 17-33 GHData/MIV-Group_SSRDEFNet-PyTorch/metric.py: 34-49 GHData/MIVRC_SSRDEFNet-PyTorch/metric.py: 34-49

Class 2576:   2 fragments, nominal size 12 lines, similarity 100%

GHData/MIV-Group_SSRDEFNet-PyTorch/metric.py: 50-68 GHData/MIVRC_SSRDEFNet-PyTorch/metric.py: 50-68

Class 2577:   2 fragments, nominal size 62 lines, similarity 100%

GHData/MIV-Group_SSRDEFNet-PyTorch/test_sr.py: 20-112 GHData/MIVRC_SSRDEFNet-PyTorch/test_sr.py: 20-112

Class 2578:   2 fragments, nominal size 12 lines, similarity 100%

GHData/MIV-Group_SSRDEFNet-PyTorch/test_disp.py: 25-40 GHData/MIVRC_SSRDEFNet-PyTorch/test_disp.py: 25-40

Class 2579:   2 fragments, nominal size 66 lines, similarity 100%

GHData/MIV-Group_SSRDEFNet-PyTorch/test_disp.py: 83-180 GHData/MIVRC_SSRDEFNet-PyTorch/test_disp.py: 83-180

Class 2580:   4 fragments, nominal size 12 lines, similarity 83%

GHData/MIV-Group_SSRDEFNet-PyTorch/common.py: 44-58 GHData/MIV-Group_SSRDEFNet-PyTorch/common.py: 66-80 GHData/MIVRC_SSRDEFNet-PyTorch/common.py: 44-58 GHData/MIVRC_SSRDEFNet-PyTorch/common.py: 66-80

Class 2581:   2 fragments, nominal size 24 lines, similarity 100%

GHData/MIV-Group_SSRDEFNet-PyTorch/common.py: 88-115 GHData/MIVRC_SSRDEFNet-PyTorch/common.py: 88-115

Class 2582:   9 fragments, nominal size 19 lines, similarity 80%

GHData/MIV-Group_SSRDEFNet-PyTorch/common.py: 117-138 GHData/Sunbaoli_PyTorch-Example/base_network.py: 40-61 GHData/Sunbaoli_PyTorch-Example/base_network.py: 265-287 GHData/Sunbaoli_PyTorch-Example/base_network.py: 5-26
GHData/MIV-Group_SSRDEFNet-PyTorch/common.py: 152-173 GHData/MIVRC_SSRDEFNet-PyTorch/common.py: 152-173 GHData/Sunbaoli_PyTorch-Example/base_network.py: 75-96 GHData/MIVRC_SSRDEFNet-PyTorch/common.py: 117-138
GHData/Sunbaoli_PyTorch-Example/base_network.py: 110-133

Class 2583:   2 fragments, nominal size 20 lines, similarity 100%

GHData/MIV-Group_SSRDEFNet-PyTorch/common.py: 188-222 GHData/MIVRC_SSRDEFNet-PyTorch/common.py: 188-222

Class 2584:   2 fragments, nominal size 24 lines, similarity 100%

GHData/MIV-Group_SSRDEFNet-PyTorch/common.py: 223-250 GHData/MIVRC_SSRDEFNet-PyTorch/common.py: 223-250

Class 2585:   2 fragments, nominal size 29 lines, similarity 89%

GHData/ElliottYan_torch_NRE/attention_PCNN.py: 28-73 GHData/ElliottYan_torch_NRE/lstm_crf_att.py: 21-66

Class 2586:   2 fragments, nominal size 15 lines, similarity 100%

GHData/ElliottYan_torch_NRE/attention_PCNN.py: 74-91 GHData/ElliottYan_torch_NRE/lstm_crf_att.py: 146-164

Class 2587:   2 fragments, nominal size 19 lines, similarity 84%

GHData/ElliottYan_torch_NRE/dataset.py: 119-141 GHData/ElliottYan_torch_NRE/dataset.py: 178-204

Class 2588:   2 fragments, nominal size 10 lines, similarity 80%

GHData/Lornatang_FSRCNN-PyTorch/imgproc.py: 354-384 GHData/Lornatang_FSRCNN-PyTorch/imgproc.py: 385-415

Class 2589:   2 fragments, nominal size 29 lines, similarity 89%

GHData/vdecaro_torch-grdn/glycans_run_exp.py: 28-61 GHData/vdecaro_torch-grdn/inex_run_exp.py: 28-60

Class 2590:   2 fragments, nominal size 12 lines, similarity 76%

GHData/mollerhoj_nnn/transformer.py: 60-72 GHData/mollerhoj_nnn/transformer.py: 84-99

Class 2591:   2 fragments, nominal size 32 lines, similarity 100%

GHData/Harryi0_dyrep_torch/dyrepHawkes_samestep.py: 8-47 GHData/Harryi0_dyrep_torch/dyrepHawkes.py: 8-47

Class 2592:   2 fragments, nominal size 14 lines, similarity 100%

GHData/Harryi0_dyrep_torch/dyrepHawkes_samestep.py: 53-71 GHData/Harryi0_dyrep_torch/dyrepHawkes.py: 53-71

Class 2593:   2 fragments, nominal size 11 lines, similarity 100%

GHData/Harryi0_dyrep_torch/dyrepHawkes_samestep.py: 72-84 GHData/Harryi0_dyrep_torch/dyrepHawkes.py: 72-84

Class 2594:   2 fragments, nominal size 157 lines, similarity 70%

GHData/Harryi0_dyrep_torch/dyrepHawkes_samestep.py: 85-302 GHData/Harryi0_dyrep_torch/dyrepHawkes.py: 85-366

Class 2595:   2 fragments, nominal size 14 lines, similarity 100%

GHData/Harryi0_dyrep_torch/dyrepHawkes_samestep.py: 336-353 GHData/Harryi0_dyrep_torch/dyrepHawkes.py: 387-404

Class 2596:   2 fragments, nominal size 16 lines, similarity 100%

GHData/Harryi0_dyrep_torch/dyrepHawkes_samestep.py: 354-370 GHData/Harryi0_dyrep_torch/dyrepHawkes.py: 405-421

Class 2597:   2 fragments, nominal size 27 lines, similarity 100%

GHData/Harryi0_dyrep_torch/dyrepHawkes_samestep.py: 371-400 GHData/Harryi0_dyrep_torch/dyrepHawkes.py: 422-451

Class 2598:   2 fragments, nominal size 34 lines, similarity 100%

GHData/Harryi0_dyrep_torch/dyrepHawkes_samestep.py: 401-448 GHData/Harryi0_dyrep_torch/dyrepHawkes.py: 452-499

Class 2599:   3 fragments, nominal size 25 lines, similarity 77%

GHData/georgeyiasemis_2D-Convolutional-Recurrent-Neural-Networks-with-PyTorch/conv2d_rnncells.py: 7-36 GHData/georgeyiasemis_2D-Convolutional-Recurrent-Neural-Networks-with-PyTorch/conv2d_rnncells.py: 79-112 GHData/georgeyiasemis_2D-Convolutional-Recurrent-Neural-Networks-with-PyTorch/conv2d_rnncells.py: 140-169

Class 2600:   2 fragments, nominal size 30 lines, similarity 100%

GHData/georgeyiasemis_2D-Convolutional-Recurrent-Neural-Networks-with-PyTorch/conv2d_rnnmodels.py: 107-143 GHData/georgeyiasemis_2D-Convolutional-Recurrent-Neural-Networks-with-PyTorch/conv2d_rnnmodels.py: 190-227

Class 2601:   4 fragments, nominal size 162 lines, similarity 72%

GHData/Xiangyu-CAS_Realtime_Multi-Person_Pose_Estimation.PyTorch/utils.py: 139-359 GHData/Xiangyu-CAS_Realtime_Multi-Person_Pose_Estimation.PyTorch/vis_util.py: 232-403 GHData/Xiangyu-CAS_Realtime_Multi-Person_Pose_Estimation.PyTorch/utils.py: 611-826 GHData/Xiangyu-CAS_Realtime_Multi-Person_Pose_Estimation.PyTorch/utils.py: 378-610

Class 2602:   2 fragments, nominal size 20 lines, similarity 100%

GHData/Xiangyu-CAS_Realtime_Multi-Person_Pose_Estimation.PyTorch/CocoFolder.py: 26-53 GHData/Xiangyu-CAS_Realtime_Multi-Person_Pose_Estimation.PyTorch/coco_loader.py: 26-53

Class 2603:   2 fragments, nominal size 22 lines, similarity 86%

GHData/Xiangyu-CAS_Realtime_Multi-Person_Pose_Estimation.PyTorch/CocoFolder.py: 54-79 GHData/Xiangyu-CAS_Realtime_Multi-Person_Pose_Estimation.PyTorch/coco_loader.py: 54-79

Class 2604:   2 fragments, nominal size 32 lines, similarity 93%

GHData/Xiangyu-CAS_Realtime_Multi-Person_Pose_Estimation.PyTorch/CocoFolder.py: 80-119 GHData/Xiangyu-CAS_Realtime_Multi-Person_Pose_Estimation.PyTorch/coco_loader.py: 80-120

Class 2605:   2 fragments, nominal size 11 lines, similarity 90%

GHData/Xiangyu-CAS_Realtime_Multi-Person_Pose_Estimation.PyTorch/CocoFolder.py: 122-133 GHData/Xiangyu-CAS_Realtime_Multi-Person_Pose_Estimation.PyTorch/coco_loader.py: 160-195

Class 2606:   2 fragments, nominal size 25 lines, similarity 96%

GHData/Xiangyu-CAS_Realtime_Multi-Person_Pose_Estimation.PyTorch/CocoFolder.py: 134-174 GHData/Xiangyu-CAS_Realtime_Multi-Person_Pose_Estimation.PyTorch/coco_loader.py: 196-238

Class 2607:   3 fragments, nominal size 53 lines, similarity 98%

GHData/gdelacruzfdez_blink-detection-torch/augmentator.py: 6-92 GHData/gdelacruzfdez_blink-detection-torch/augmentator.py: 98-181 GHData/gdelacruzfdez_blink-detection-torch/augmentator.py: 188-275

Class 2608:   2 fragments, nominal size 31 lines, similarity 75%

GHData/gdelacruzfdez_blink-detection-torch/new-transformer-lightning.py: 52-107 GHData/gdelacruzfdez_blink-detection-torch/cnn-transformer-lightning.py: 48-93

Class 2609:   2 fragments, nominal size 13 lines, similarity 100%

GHData/gdelacruzfdez_blink-detection-torch/new-transformer-lightning.py: 149-162 GHData/gdelacruzfdez_blink-detection-torch/cnn-transformer-lightning.py: 121-134

Class 2610:   2 fragments, nominal size 16 lines, similarity 81%

GHData/gdelacruzfdez_blink-detection-torch/new-transformer-lightning.py: 163-186 GHData/gdelacruzfdez_blink-detection-torch/cnn-transformer-lightning.py: 135-175

Class 2611:   2 fragments, nominal size 25 lines, similarity 92%

GHData/gdelacruzfdez_blink-detection-torch/new-transformer-lightning.py: 232-270 GHData/gdelacruzfdez_blink-detection-torch/cnn-transformer-lightning.py: 229-266

Class 2612:   2 fragments, nominal size 39 lines, similarity 94%

GHData/gdelacruzfdez_blink-detection-torch/new-transformer-lightning.py: 271-318 GHData/gdelacruzfdez_blink-detection-torch/cnn-transformer-lightning.py: 267-313

Class 2613:   5 fragments, nominal size 41 lines, similarity 81%

GHData/gdelacruzfdez_blink-detection-torch/lstm_main.py: 15-62 GHData/gdelacruzfdez_blink-detection-torch/siamese_main.py: 12-53 GHData/gdelacruzfdez_blink-detection-torch/lstm_cnn_main.py: 15-62 GHData/gdelacruzfdez_blink-detection-torch/transformer_main.py: 15-62 GHData/gdelacruzfdez_blink-detection-torch/cnn_main.py: 12-56

Class 2614:   2 fragments, nominal size 21 lines, similarity 80%

GHData/gdelacruzfdez_blink-detection-torch/evaluator.py: 162-183 GHData/gdelacruzfdez_blink-detection-torch/evaluator.py: 184-204

Class 2615:   2 fragments, nominal size 65 lines, similarity 88%

GHData/YaddaShang_deepvo_torch/loss_functions_new.py: 24-123 GHData/YaddaShang_deepvo_torch/MDSI.py: 10-98

Class 2616:   2 fragments, nominal size 31 lines, similarity 73%

GHData/eran505_nnPyTorch/xgb_model.py: 40-70 GHData/eran505_nnPyTorch/xgb_model.py: 71-119

Class 2617:   2 fragments, nominal size 29 lines, similarity 100%

GHData/smiler96_GMM-KMeans-PyTorch/GMM-Pytorch.py: 22-60 GHData/smiler96_GMM-KMeans-PyTorch/KMeans-Batch.py: 20-57

Class 2618:   2 fragments, nominal size 14 lines, similarity 78%

GHData/smiler96_GMM-KMeans-PyTorch/GMM-Pytorch.py: 110-134 GHData/smiler96_GMM-KMeans-PyTorch/GMM-Pytorch.py: 253-277

Class 2619:   2 fragments, nominal size 29 lines, similarity 96%

GHData/vidyadhariGithub_NeuralNetworks_project1/kuzu_main.py: 58-103 GHData/ShawnFrost23_KMNIST-pyTorch/kuzu_main.py: 57-100

Class 2620:   2 fragments, nominal size 17 lines, similarity 100%

GHData/vidyadhariGithub_NeuralNetworks_project1/spiral_main.py: 13-32 GHData/visbond_TwinSpirals/spiral_main.py: 12-31

Class 2621:   2 fragments, nominal size 13 lines, similarity 100%

GHData/vidyadhariGithub_NeuralNetworks_project1/spiral_main.py: 33-51 GHData/visbond_TwinSpirals/spiral_main.py: 32-50

Class 2622:   2 fragments, nominal size 14 lines, similarity 71%

GHData/chagmgang_torch_distributed_reinforcement_learning/impala.py: 20-37 GHData/chagmgang_torch_distributed_reinforcement_learning/apex.py: 20-39

Class 2623:   2 fragments, nominal size 39 lines, similarity 70%

GHData/chagmgang_torch_distributed_reinforcement_learning/impala.py: 129-175 GHData/chagmgang_torch_distributed_reinforcement_learning/apex.py: 164-214

Class 2624:   2 fragments, nominal size 55 lines, similarity 92%

GHData/hefan1_PyTorch/CUB_loader.py: 16-81 GHData/hefan1_PyTorch/CGLoader.py: 14-82

Class 2625:   2 fragments, nominal size 13 lines, similarity 76%

GHData/pyxploiter_Table-Detection-PyTorch/models.py: 10-28 GHData/pyxploiter_Table-Detection-PyTorch/models.py: 72-92

Class 2626:   2 fragments, nominal size 30 lines, similarity 100%

GHData/pyxploiter_Table-Detection-PyTorch/models.py: 29-71 GHData/pyxploiter_Table-Detection-PyTorch/models.py: 93-134

Class 2627:   2 fragments, nominal size 17 lines, similarity 100%

GHData/Ravitha_pyTorch_Examples/Ex8_Transfer_Finetune.py: 29-46 GHData/Ravitha_pyTorch_Examples/Ex9_Transfer_FeatureExtractor.py: 29-46

Class 2628:   2 fragments, nominal size 22 lines, similarity 100%

GHData/Ravitha_pyTorch_Examples/Ex8_Transfer_Finetune.py: 47-70 GHData/Ravitha_pyTorch_Examples/Ex9_Transfer_FeatureExtractor.py: 47-70

Class 2629:   2 fragments, nominal size 31 lines, similarity 90%

GHData/Ravitha_pyTorch_Examples/Ex8_Transfer_Finetune.py: 71-112 GHData/Ravitha_pyTorch_Examples/Ex9_Transfer_FeatureExtractor.py: 71-119

Class 2630:   4 fragments, nominal size 27 lines, similarity 85%

GHData/Ravitha_pyTorch_Examples/Unet.py: 25-59 GHData/Ravitha_pyTorch_Examples/Ex1_Seg_FCN.py: 25-59 GHData/Ravitha_pyTorch_Examples/Ex1_Seg_FCN.py: 60-90 GHData/Ravitha_pyTorch_Examples/Unet.py: 60-90

Class 2631:   2 fragments, nominal size 13 lines, similarity 100%

GHData/Ravitha_pyTorch_Examples/Unet.py: 104-118 GHData/Ravitha_pyTorch_Examples/Ex1_Seg_FCN.py: 104-118

Class 2632:   2 fragments, nominal size 20 lines, similarity 100%

GHData/Ravitha_pyTorch_Examples/Unet.py: 212-232 GHData/Ravitha_pyTorch_Examples/Ex1_Seg_FCN.py: 162-182

Class 2633:   2 fragments, nominal size 29 lines, similarity 100%

GHData/Ravitha_pyTorch_Examples/Unet.py: 233-263 GHData/Ravitha_pyTorch_Examples/Ex1_Seg_FCN.py: 183-213

Class 2634:   2 fragments, nominal size 10 lines, similarity 100%

GHData/bollakarthikeya_LeNet-5-PyTorch/lenet5_cpu.py: 112-132 GHData/bollakarthikeya_LeNet-5-PyTorch/lenet5_gpu.py: 99-119

Class 2635:   2 fragments, nominal size 49 lines, similarity 92%

GHData/wnsgur1198_clothing_category_attribute_classification-with_torch/setup.py: 35-108 GHData/thuyngch_ATSS-EfficientDet-PyTorch/setup.py: 113-191

Class 2636:   2 fragments, nominal size 19 lines, similarity 84%

GHData/Sunda233_DCGAN-PyTorch/DCGAN.py: 49-70 GHData/Lornatang_PyTorch-InfoGAN/infogan.py: 120-146

Class 2637:   2 fragments, nominal size 47 lines, similarity 91%

GHData/wutianyiRosun_Deeplab_PyTorch/train.py: 43-96 GHData/txrc_PyTorch-DeepLab-Berkeley/train.py: 43-93

Class 2638:   2 fragments, nominal size 15 lines, similarity 100%

GHData/wutianyiRosun_Deeplab_PyTorch/train.py: 115-139 GHData/txrc_PyTorch-DeepLab-Berkeley/train.py: 112-136

Class 2639:   3 fragments, nominal size 15 lines, similarity 100%

GHData/wutianyiRosun_Deeplab_PyTorch/evaluate.py: 30-51 GHData/wutianyiRosun_Deeplab_PyTorch/evaluate_msc.py: 29-50 GHData/txrc_PyTorch-DeepLab-Berkeley/evaluate.py: 35-56

Class 2640:   2 fragments, nominal size 13 lines, similarity 100%

GHData/liteworldz_torch-adversarial/pgd_adversarial_training.py: 43-57 GHData/liteworldz_torch-adversarial/interpolated_adversarial_training.py: 55-68

Class 2641:   4 fragments, nominal size 38 lines, similarity 72%

GHData/liteworldz_torch-adversarial/pgd_adversarial_training.py: 104-153 GHData/liteworldz_torch-adversarial/test.py: 31-71 GHData/liteworldz_torch-adversarial/interpolated_adversarial_training.py: 131-180 GHData/liteworldz_torch-adversarial/interpolated_adversarial_training.py: 85-130

Class 2642:   6 fragments, nominal size 17 lines, similarity 72%

GHData/data-race_torch_dist_demo/ddp.py: 15-36 GHData/data-race_torch_dist_demo/standalone.py: 13-34 GHData/data-race_torch_dist_demo/dp.py: 13-34
GHData/data-race_torch_dist_demo/ddp.py: 37-52 GHData/data-race_torch_dist_demo/dp.py: 35-50 GHData/data-race_torch_dist_demo/standalone.py: 35-50

Class 2643:   2 fragments, nominal size 26 lines, similarity 96%

GHData/data-race_torch_dist_demo/standalone.py: 51-83 GHData/data-race_torch_dist_demo/dp.py: 51-84

Class 2644:   2 fragments, nominal size 18 lines, similarity 100%

GHData/201419_Optimizer-PyTorch/adabound.py: 25-44 GHData/201419_Optimizer-PyTorch/adabound.py: 139-158

Class 2645:   4 fragments, nominal size 42 lines, similarity 71%

GHData/201419_Optimizer-PyTorch/adabound.py: 50-119 GHData/201419_Optimizer-PyTorch/omd.py: 85-145 GHData/201419_Optimizer-PyTorch/adabound.py: 164-234 GHData/201419_Optimizer-PyTorch/extragradient.py: 202-248

Class 2646:   3 fragments, nominal size 13 lines, similarity 100%

GHData/201419_Optimizer-PyTorch/extragradient.py: 183-196 GHData/201419_Optimizer-PyTorch/adam.py: 27-40 GHData/201419_Optimizer-PyTorch/omd.py: 66-79

Class 2647:   2 fragments, nominal size 16 lines, similarity 100%

GHData/201419_Optimizer-PyTorch/errorfeedbacksgd.py: 184-201 GHData/201419_Optimizer-PyTorch/errorfeedbacksgd.py: 202-219

Class 2648:   2 fragments, nominal size 12 lines, similarity 76%

GHData/phoenix-meadowlark_torchscript_ir/generate_torchvision_ir.py: 11-22 GHData/phoenix-meadowlark_torchscript_ir/generate_huggingface_ir.py: 47-62

Class 2649:   2 fragments, nominal size 35 lines, similarity 91%

GHData/ZhenyueQin_Implementation-MolGAN-PyTorch/args.py: 8-68 GHData/ZhenyueQin_Implementation-MolGAN-PyTorch/args.py: 69-124

Class 2650:   2 fragments, nominal size 30 lines, similarity 100%

GHData/ZhenyueQin_Implementation-MolGAN-PyTorch/main_vae.py: 19-60 GHData/ZhenyueQin_Implementation-MolGAN-PyTorch/main_gan.py: 19-60

Class 2651:   2 fragments, nominal size 32 lines, similarity 87%

GHData/ZhenyueQin_Implementation-MolGAN-PyTorch/solver_vae.py: 24-77 GHData/ZhenyueQin_Implementation-MolGAN-PyTorch/solver_gan.py: 21-74

Class 2652:   2 fragments, nominal size 11 lines, similarity 100%

GHData/ZhenyueQin_Implementation-MolGAN-PyTorch/solver_vae.py: 95-107 GHData/ZhenyueQin_Implementation-MolGAN-PyTorch/solver_gan.py: 97-109

Class 2653:   2 fragments, nominal size 14 lines, similarity 100%

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